library(pander)
library(tidyr)
library(compute.es)
library(metafor)
library(plyr)
library(dplyr)
library(lme4)
library(car)
library(forestplot)
library(ggplot2)
library(ggthemes)
library(kableExtra)
library(ggrepel)
library(reshape2)
library(RColorBrewer)
library(ggridges)
library(rstan) #Note that installation requires some effort: dependency for brms
library(brms) 
library(backports) #seems to be a dependency
library(bayesplot)
#devtools::install_github("mvuorre/brmstools")
library(brmstools)
library(metaAidR)
source("I2_function.R") #Adapted function for obtaining I2 with CIs
source("Vdodge_function.R") # nice function for ggplot
source("Tidy_functions_for_brms.R") #Tidy functions for making model tables for brms

Supplementary Methods

Our aim was to investigate the effects of sexual selection on population fitness by conducting a meta-analysis on studies that measured fitness related outcomes after experimentally evolving a population under varying levels of opportunity for sexual selection. Here we describe the process of the literature search, data extraction, effect size calculation, formulation of multilevel models and assessing publication bias. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) as a guide during this meta-analysis.

Inclusion/Exclusion criteria

After removing duplicates papers recovered from both Web of Science and Scopus, we read the titles and abstracts of the remaining 1015 papers, and removed papers that were not relevant (typically because they were not an empirical study using experimental evolution). This left 130 papers, for which we read the full text and applied the following selection criteria:

  • (1: Study Design) The study was an experimental evolution study lasting >1 generation
  • (2: Population) a) The study was conducted using an animal species that was b) dioecious
  • (3: Intervention and Control) The study experimentally manipulated the strength of sexual selection for at least one generation (e.g. via enforced monogamy or an altered sex ratio)
  • (4: Outcomes) The study measured a trait that we judged to be a potential correlate of population fitness.

This latter criterion is likely to be contentious, because there is rarely enough data justify the assumption that a particular trait is (or is not) correlated with population fitness. We therefore relied on our best judgement when deciding which studies to exclude (see Table S1). The inclusion/exlusion critera as applied to each study are detailed in Table S2.

Table S1: We classed each of the twenty fitness related outcomes into three broad groups of direct, indirect and ambiguous based on the established link with population fitness, the directionality of the measure. Here we detailed how these outcomes were measured in the studies of this meta-analysis. In the accompanying box we provide a legend to the references cited in the table.



outcome.descriptions <- read.csv('data/outcome.descriptions.csv', 
                                 fileEncoding="UTF-8")
kable(outcome.descriptions, "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "500px")
Outcome Classification Explanation
Behavioural Plasticity Ambiguous Female kicking against male harassment in different sociosexual contexts for the beetle Callosobruchus maculatus (1)
Body Size Ambiguous Body size was often recorded to correct for other morphometric traits (e.g. body condition, strength or testes weight (2, 3). It was measured as either length or dry mass.
Development Rate Ambiguous Egg-to adult development time was recorded in several studies (4-6) and often alongside traits other life-history traits suspected to impact fitness.
Early Fecundity Ambiguous Early fecundity was measured (alongside lifetime fecundity) as a life-history trait that may impact lifetime reproductive success. It was defined as either the total or proportional reproductive output in earlier stages of maturity (e.g. within the first 7 days) (7, 8).
Immunity Ambiguous Phenoloxidase (PO) activity or parasite load (6, 9-11).
Mating Duration Ambiguous Mating duration may have variable fitness impacts based on the soiciosexual conditions and extent of sexual conflict. It may be beneficial to have longer mating bouts for a male in a competitive environment however it may be damaging for a female under benign conditions (e.g. 1, 12, 13, 14).
Pesticide Resistance Ambiguous Pesticide resistance was measured both in the presence and absence of pesticides for the insect Tribolium castaneum, it was a binary measure of resistance to knockdown that was incorporated into generalized linear mixed models (15),
Mutant Frequency Indirect Allele and mutant frequency measured at the population level (Arbuthnott and Rundle (16), Hollis, Fierst (17))
Body Condition Indirect Mean body weight of Onthophagus Taurus adjusted for body size (thorax width) (Simmons and Garcia-Gonzalez (2).
Fitness Senescence Indirect Rate of decline in survival probability across lifespan (5, 18).
Lifespan Indirect Longevity or survival across the entire lifespan (e.g. 19) or from a given point once under stressful conditions, such as starvation or after females mated in different operational sex ratios (e.g. 20).
Male Attractiveness Indirect Inferred from female preference tests in mice (21, 22) and male ornament size (coloration) in guppies (23).
Mating Frequency Indirect Number of mounts by males on females in Tribolium castaneum and Drosophila melanogaster (13, 19).
Mating Latency Indirect Time taken for a male to undertake their first copulatory mount from the time of being first put together with female/s (1, 12-14, 24, 25).
Mating Success Indirect Male mating success measured males ability to successfully mate with females. Often in the presence of other males (e.g. 8, 24, 26). Mating success of a male against a rival male can be determined via competing a focal male against an irradiated (infertile) competitor, the resulting proportion of eggs hatching are then determined to be a measure of the focal males success. Mating success also included measurements of mating capacity where males were continually presented with females until exhausted, the number of sequential matings were then recorded (27) and mating offence and defence ability (14). The mating offence and defence capability was estimated via paternity share of a male when in the first mating position (P1) or the second (P2).
Strength Indirect Male pulling strength in the dung beetle, Onthophagus Taurus, measured by attaching weights and measuring the weight the beetle was able to pull (Almbro and Simmons (3) .
Ejaculate Quality and Production Indirect Sperm quality and production grouped multiple measured outcomes together, both within a study (28) and during the meta-analysis. This includes sperm size, plug size, testes size, soporific effect, ejaculate weight, accessory gland size, motility, path velocity, sperm longevity (e.g. 1, 29, 30, 31)
Extinction Rate Direct Extinction rate was measured at the population level, either via recording the proportion of extinct lines after a given number of generations (32, 33) or via analysis of extinction rate over consecutive generations via the Weibull baseline hazard distribution (34).
Offspring Viability Direct Offspring viability, also recorded as egg-to-adult viability or embryonic viability, was measured as survival to a certain age (e.g. 1 year (23)) or life stage (e.g. hatching (35)).
Reproductive Success Direct A measure of the number of offspring produced by an individual. Reproductive success was also described as fecundity (e.g. 36), number of offspring produced (e.g. 37), fertility (e.g. 12) in females and proportion or total progeny sired in males (e.g. 5).



outcome.references <- read.csv('data/references.tableS1.csv', 
                                 fileEncoding="UTF-8")
kable(outcome.references, "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "250px")
Citation Reference
1 van Lieshout E, McNamara KB, Simmons LW. Rapid Loss of Behavioral Plasticity and Immunocompetence under Intense Sexual Selection. Evolution. 2014;68(9):2550-8.
2 Simmons LW, Garcia-Gonzalez F. Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles. Evolution. 2008;62(10):2580-91.
3 Almbro M, Simmons LW. Sexual Selection Can Remove an Experimentally Induced Mutation Load. Evolution. 2014;68(1):295-300.
4 Fricke C, Arnqvist G. Rapid adaptation to a novel host in a seed beetle (Callosobruchus maculatus): The role of sexual selection. Evolution. 2007;61(2):440-54.
5 Hollis, B. and T. J. Kawecki. 2014. Male cognitive performance declines in the absence of sexual selection. Proc. R. Soc. B-Biol. Sci. 281.
6 McKean KA, Nunney L. Sexual selection and immune function in Drosophila melanogaster. Evolution. 2008;62(2):386-400.
7 Crudgington HS, Fellows S, Snook RR. Increased opportunity for sexual conflict promotes harmful males with elevated courtship frequencies. Journal of Evolutionary Biology. 2010;23(2):440-6.
8 Tilszer, M. Antoszczyk, K. Salek, N. Zajac, E. Radwan, J.. Evolution under relaxed sexual conflict in the bulb mite Rhizoglyphus robini. Evolution. 2006;60(9):1868-73.
9 Hangartner S, Michalczyk L, Gage MJG, Martin OY. Experimental removal of sexual selection leads to decreased investment in an immune component in female Tribolium castaneum. Infection, Genetics and Evolution. 2015;33:212-8.
10 Hangartner S, Sbilordo SH, Michalczyk L, Gage MJG, Martin OY. Are there genetic trade-offs between immune and reproductive investments in Tribolium castaneum? Infection, Genetics and Evolution. 2013;19:45-50.
11 McNamara KB, van Lieshout E, Simmons LW. A test of the sexy-sperm and good-sperm hypotheses for the evolution of polyandry. Behavioral Ecology. 2014;25(4):989-95.
12 Edward DA, Fricke C, Chapman T. Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection. Philosophical Transactions of the Royal Society B-Biological Sciences. 2010;365(1552):2541-8.
13 Michalczyk L, Millard AL, Martin OY, Lumley AJ, Emerson BC, Gage MJG. Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum. Evolution. 2011;65(3):713-24.
14 Nandy B, Chakraborty P, Gupta V, Ali SZ, Prasad NG. Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio. Evolution. 2013;67(7):2133-41.
15 Jacomb F, Marsh J, Holman L. Sexual selection expedites the evolution of pesticide resistance. Evolution. 2016;70(12):2746-51.
16 Arbuthnott D, Rundle HD. Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster. Evolution. 2012;66(7):2127-37.
17 Hollis B, Fierst JL, Houle D. Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster. Evolution. 2009;63(2):324-33.
18 Archer CR, Duffy E, Hosken DJ, Mokkonen M, Okada K, Oku K, et al. Sex-specific effects of natural and sexual selection on the evolution of life span and ageing in Drosophila simulans. Functional Ecology. 2015;29(4):562-9.
19 Wigby S, Chapman T. Female resistance to male harm evolves in response to manipulation of sexual conflict. Evolution. 2004;58(5):1028-37.
20 Martin OY, Hosken DJ. Costs and benefits of evolving under experimentally enforced polyandry or monogamy. Evolution. 2003;57(12):2765-72.
21 Firman RC. Female social preference for males that have evolved via monogamy: evidence of a trade-off between pre- and post-copulatory sexually selected traits? Biology Letters. 2014;10(10).
22 Nelson AC, Colson KE, Harmon S, Potts WK. Rapid adaptation to mammalian sociality via sexually selected traits. Bmc Evolutionary Biology. 2013;13.
23 Pelabon C, Larsen LK, Bolstad GH, Viken A, Fleming IA, Rosenqvist G. The effects of sexual selection on life-history traits: An experimental study on guppies. Journal of Evolutionary Biology. 2014;27(2):404-16.
24 Debelle A, Ritchie MG, Snook RR. Sexual selection and assortative mating: an experimental test. Journal of Evolutionary Biology. 2016;29(7):1307-16.
25 Hollis B, Kawecki TJ. Male cognitive performance declines in the absence of sexual selection. Proceedings of the Royal Society B-Biological Sciences. 2014;281(1781).
26 McGuigan K, Petfield D, Blows MW. Reducing mutation load through sexual selection on males. Evolution. 2011;65(10):2816-29.
27 Crudgington HS, Fellows S, Badcock NS, Snook RR. Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment. Evolution. 2009;63(4):926-38.
28 Firman RC, Simmons LW. Experimental Evolution of Sperm Quality Via Postcopulatory Sexual Selection in House Mice. Evolution. 2010;64(5):1245-56.
29 Fritzsche K, Timmermeyer N, Wolter M, Michiels NK. Female, but not male, nematodes evolve under experimental sexual coevolution. Proceedings of the Royal Society B-Biological Sciences. 2014;281(1796).
30 Gay L, Hosken DJ, Vasudev R, Tregenza T, Eady PE. Sperm competition and maternal effects differentially influence testis and sperm size in Callosobruchus maculatus. Journal of Evolutionary Biology. 2009;22(5):1143-50.
31 McNamara KB, Robinson SP, Rosa ME, Sloan NS, van Lieshout E, Simmons LW. Male-biased sex ratio does not promote increased sperm competitiveness in the seed beetle, Callosobruchus maculatus. Scientific Reports. 2016;6.
32 Jarzebowska M, Radwan J. Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites. Evolution. 2010;64(5):1283-9.
33 Plesnar-Bielak A, Skrzynecka AM, Prokop ZM, Radwan J. Mating system affects population performance and extinction risk under environmental challenge. Proceedings of the Royal Society B-Biological Sciences. 2012;279(1747):4661-7.
34 Lumley AJ, Michalczyk L, Kitson JJN, Spurgin LG, Morrison CA, Godwin JL, et al. Sexual selection protects against extinction. Nature. 2015;522(7557):470-+.
35 Plesnar A, Konior M, Radwan J. The role of sexual selection in purging the genome of induced mutations in the bulb mite (Rizoglyphus robini). Evolutionary Ecology Research. 2011;13(2):209-16.
36 Firman RC. Polyandrous females benefit by producing sons that achieve high reproductive success in a competitive environment. Proceedings of the Royal Society B-Biological Sciences. 2011;278(1719):2823-31.
37 Bernasconi G, Keller L. Female polyandry affects their sons’ reproductive success in the red flour beetle Tribolium castaneum. Journal of Evolutionary Biology. 2001;14(1):186-93.



Table S2: An eligibility criteria was based on four features a study needed to include (discussed above), to be eligable for inclusion in the meta-analysis the study needed to satisfy all criteria. Here we applied a step-wise process to the studies that had their full-text read and excluded them when they first failed to meet the criteria. Additional notes documenting reasons behind exclusion were also taken.

Eligibility.criteria <- read.csv('data/Eligibility Workbook(22.02).csv', 
                                 fileEncoding="UTF-8")
kable(Eligibility.criteria, "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "500px")
Authors Year Title Study.Design Population Intervention.and.Control Outcomes Included Exclusion.Reason Notes
Aguirre, J. D. and D. J. Marshall 2012 Does Genetic Diversity Reduce Sibling Competition? No No 1 Not an experimental evolution study: full-sib/half-sib breeding design
Ahuja, A. and R. S. Singh 2008 Variation and evolution of male sex combs in Drosophila: Nature of selection response and theories of genetic variation for sexual traits No No 1 Artificial selection was conducted
Almbro, M. and L. W. Simmons 2014 Sexual Selection Can Remove an Experimentally Induced Mutation Load Yes Yes Yes Yes Yes Male strength is important in male-male competition
Amitin, E. G. and S. Pitnick 2007 Influence of developmental environment on male- and female-mediated sperm precedence in Drosophila melanogaster Yes Yes No No 3 Larval density was the intervention: not strength of sexual selection
Antolin, M. F., P. J. Ode, G. E. Heimpel, R. B. O’Hara and M. R. Strand 2003 Population structure, mating system, and sex-determining allele diversity of the parasitoid wasp Habrobracon hebetor No No 1 Not experimental evolution: Lab rearing of wild populations with eventual genetic analysis
Arbuthnott, D., E. M. Dutton, A. F. Agrawal and H. D. Rundle 2014 The ecology of sexual conflict: ecologically dependent parallel evolution of male harm and female resistance in Drosophila melanogaster Yes Yes No No 3 Intervention was either ethanol or cadmium mixture
Arbuthnott, D. and H. D. Rundle 2012 Sexual Selection Is Ineffectual or Inhibits the Purging of Deleterious Mutations in Drosophila Melanogaster Yes Yes Yes Yes Yes Natural selection acted against known deleterious alleles, thus indicate fitness aspect
Arbuthnott, D. and H. D. Rundle 2014 Misalignment of natural and sexual selection among divergently adapted Drosophila melanogaster populations Yes Yes No No 3 Intervention was either ethanol or cadmium mixture
Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Sex-specific effects of natural and sexual selection on the evolution of life span and ageing in Drosophila simulans Yes Yes Yes Yes Yes Natural selection was measured simultanous and thus provides measurement of suitability of phenotype to environment
Artieri, C. G., W. Haerty, B. P. Gupta and R. S. Singh 2008 Sexual selection and maintenance of sex: Evidence from comparisons of rates of genomic accumulation of mutations and divergence of sex-related genes in sexual and hermaphroditic species of Caenorhabditis No No 1 Comparative genomic approach
Bacigalupe, L. D., H. S. Crudgington, F. Hunter, A. J. Moore and R. R. Snook 2007 Sexual conflict does not drive reproductive isolation in experimental populations of Drosophila pseudoobscura Yes Yes Yes No No 4 Viability and sterility were measured as well as mating speed, however these were in crosses, refer to 2008 study for beater outcomes
Bacigalupe, L. D., H. S. Crudgington, J. Slate, A. J. Moore and R. R. Snook 2008 Sexual selection and interacting phenotypes in experimental evolution: A study of Drosophila pseudoobscura mating behavior Yes Yes Yes Yes No Data not suitable Mating speed cited as a measure of fitness. Because of the crossses the data is not able to be extracted to an effect size that is comprable to other studies
Barbosa, M., S. R. Connolly, M. Hisano, M. Dornelas and A. E. Magurran 2012 Fitness consequences of female multiple mating: A direct test of indirect benefits No No 1 Measures multiple mating not experimental evolution with sexual selection treatments
Bernasconi, G. and L. Keller 2001 Female polyandry affects their sons’ reproductive success in the red flour beetle Tribolium castaneum Yes Yes Yes Yes Yes Polyandry was done sequentially with postcop mate choice.
Bielak, A. P., A. M. Skrzynecka, K. Miler and J. Radwan 2014 Selection for alternative male reproductive tactics alters intralocus sexual conflict No No 1 Artificial selection was conducted
Blows, M. W. 2002 Interaction between natural and sexual selection during the evolution of mate recognition Yes Yes Yes No No 4 Hybrid Drosophilia used, indirect fitness was measured (mate recognition system)
Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman 2012 Interactions between Genotype and Sexual Conflict Environment Influence Transgenerational Fitness in Drosophila Melanogaster Yes Yes Yes Yes Yes Multiple males but only one at a time: still is post copulatory SS, so included
Castillo, D. M., M. K. Burger, C. M. Lively and L. F. Delph 2015 Experimental evolution: Assortative mating and sexual selection, independent of local adaptation, lead to reproductive isolation in the nematode Caenorhabditis remanei Yes Yes No No 3 No SS lines
Cayetano, L., A. A. Maklakov, R. C. Brooks and R. Bonduriansky 2011 Evolution of Male and Female Genitalia Following Release from Sexual Selection Yes Yes Yes No No 4 Conflict / burdensome and defensive / offensive traits have fitness costs and benefits: Removing as too difficult to see clear fitness of measurements
Chandler, C. H., C. Ofria and I. Dworkin 2013 Runaway Sexual Selection Leads to Good Genes Yes No No 2a Digital organisms used
Chenoweth, S. F., N. C. Appleton, S. L. Allen and H. D. Rundle 2015 Genomic Evidence that Sexual Selection Impedes Adaptation to a Novel Environment Yes Yes Yes No No 4 Alongside direct fitness, SNPs also used. This paper reports SNPs while Rundle (2006) reports fitness measures. Thus data is extracted from that paper, not this one
Chenoweth, S. F., D. Petfield, P. Doughty and M. W. Blows 2007 Male choice generates stabilizing sexual selection on a female fecundity correlate No No 1 Behavioural mate choice experiment
Chenoweth, S. F., H. D. Rundle and M. W. Blows 2008 Genetic constraints and the evolution of display trait sexual dimorphism by natural and sexual selection Yes Yes Yes No No 4 Natural selection was also measured and CHCs provide an indirect fitness aspect but too difficult to compare (CHCs were not used as outcome in this meta-analysis)
Chenoweth, S. F., H. D. Rundle and M. W. Blows 2010 Experimental evidence for the evolution of indirect genetic effects: changes in the interaction effect coefficient, psi (_), due to sexual selection Yes Yes Yes No No 4 CHCs may provide indirect fitness aspect but are very difficult measures to compare or turn into effect sizes
Crudgington, H. S., A. P. Beckerman, L. Brustle, K. Green and R. R. Snook 2005 Experimental removal and elevation of sexual selection: Does sexual selection generate manipulative males and resistant females? Yes Yes Yes Yes Yes
Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Experimental Manipulation of Sexual Selection Promotes Greater Male Mating Capacity but Does Not Alter Sperm Investment Yes Yes Yes Yes Yes Appears to measure more direct and indirect outcomes
Crudgington, H. S., S. Fellows and R. R. Snook 2010 Increased opportunity for sexual conflict promotes harmful males with elevated courtship frequencies Yes Yes Yes Yes Yes
Debelle, A., M. G. Ritchie and R. R. Snook 2016 Sexual selection and assortative mating: an experimental test Yes Yes Yes Yes Yes
Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Experimental Removal of Sexual Selection Reveals Adaptations to Polyandry in Both Sexes Yes Yes Yes Yes Yes
Edward, D. A., C. Fricke and T. Chapman 2010 Adaptations to sexual selection and sexual conflict: insights from experimental evolution and artificial selection Yes Yes Yes Yes Yes
Fava, G. 1975 Studies on the selective agents operating in experimental populations of Tisbe clodiensis (Copepoda, Harpacticoida) Yes Yes No No 3 No difference in SS between treatments: Instead different genotype frequencies.
Firman, R. C. 2011 Polyandrous females benefit by producing sons that achieve high reproductive success in a competitive environment Yes Yes Yes Yes Yes It looks like post copulatory selection was used here
Firman, R. C. 2014 Female social preference for males that have evolved via monogamy: evidence of a trade-off between pre- and post-copulatory sexually selected traits? Yes Yes Yes Yes Yes The outcome measured was female preference and male scent marking rate. Likely to have a role in fitness but not explicitly stated
Firman, R. C., L. Y. Cheam and L. W. Simmons 2011 Sperm competition does not influence sperm hook morphology in selection lines of house mice Yes Yes Yes Yes Yes Sperm quality was measured
Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons 2015 Evolutionary change in testes tissue composition among experimental populations of house mice Yes Yes Yes Yes Yes Amount of sperm producing tissue was measured as it provides an advantage in sperm competition
Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 The Coevolution of Ova Defensiveness with Sperm Competitiveness in House Mice Yes Yes Yes Yes Yes Ova defensivenenss can bias fertilization to a more specific type of sperm and thus be a fitness adavantage
Firman, R. C. and L. W. Simmons 2010 Experimental Evolution of Sperm Quality Via Postcopulatory Sexual Selection in House Mice Yes Yes Yes Yes Yes Polygamous lines have only post-copulatory selection
Firman, R. C. and L. W. Simmons 2011 Experimental evolution of sperm competitiveness in a mammal Yes Yes Yes Yes Yes Sperm competition is a fitness advantage
Firman, R. C. and L. W. Simmons 2012 Male house mice evolving with post-copulatory sexual selection sire embryos with increased viability Yes Yes Yes Yes Yes Post cop SS used
Fricke, C., C. Andersson and G. Arnqvist 2010 Natural selection hampers divergence of reproductive traits in a seed beetle Yes Yes Yes No No 4 Could not use the broad outcome of reproductive characteristics as it is not directional
Fricke, C. and G. Arnqvist 2007 Rapid adaptation to a novel host in a seed beetle (Callosobruchus maculatus): The role of sexual selection Yes Yes Yes Yes Yes Post cop SS used
Fritzsche, K., I. Booksmythe and G. Arnqvist 2016 Sex Ratio Bias Leads to the Evolution of Sex Role Reversal in Honey Locust Beetles Yes Yes Yes Yes Yes Male bias and female bias setups without monogamus/lack of SS
Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Female, but not male, nematodes evolve under experimental sexual coevolution Yes Yes Yes Yes Yes Male bias and female bias setups without monogamus/lack of SS
Garcia-Gonzalez, F., Y. Yasui and J. P. Evans 2015 Mating portfolios: bet-hedging, sexual selection and female multiple mating Yes Yes Yes Yes No Data not suitable Experiments run alongside bet-hedging, perhaps confounding and not able to be placed alongside other studies in this meta-analysis
Gay, L., P. E. Eady, R. Vasudev, D. J. Hosken and T. Tregenza 2009 Does reproductive isolation evolve faster in larger populations via sexually antagonistic coevolution? Yes Yes No No 3 Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. This experiment was focussed on reproductive isolation anyway
Gay, L., D. J. Hosken, P. Eady, R. Vasudev and T. Tregenza 2011 The Evolution of Harm-Effect of Sexual Conflicts and Population Size Yes Yes No No 3 Generations of monoandry were replaced by polyandry (not done simultaneously ), Not sure whether the monogamous lines were maintained. Also, did not directly look at SS+ vs SS-
Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady 2009 Sperm competition and maternal effects differentially influence testis and sperm size in Callosobruchus maculatus Yes Yes Yes Yes Yes Appears to be direct comparison bw monogamous and polygamous structures
Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin 2014 Environmental quality alters female costs and benefits of evolving under enforced monogamy Yes Yes Yes Yes Yes Direct Measures of fitness in environments that had standard and sub-standard food quality
Grieshop, K., J. Stangberg, I. Martinossi-Allibert, G. Arnqvist and D. Berger 2016 Strong sexual selection in males against a mutation load that reduces offspring production in seed beetles Yes Yes No No 3 Different mating systems/ opportunity for SS were not imposed
Hall, M. D., L. F. Bussiere and R. Brooks 2009 Diet-dependent female evolution influences male lifespan in a nuptial feeding insect Yes Yes No No 3 Different mating systems/ opportunity for SS were not imposed
Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Experimental removal of sexual selection leads to decreased investment in an immune component in female Tribolium castaneum Yes Yes Yes Yes Yes
Hangartner, S., S. H. Sbilordo, L. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Are there genetic trade-offs between immune and reproductive investments in Tribolium castaneum? Yes Yes Yes Yes Yes Different levels of SS, but none with enforced monogamy (no choice)
Hicks, S. K., K. L. Hagenbuch and L. M. Meffert 2004 Variable costs of mating, longevity, and starvation resistance in Musca domestica (Diptera: Muscidae) Yes Yes No No 3 Study on environmental conditions not SS treatment
Holland, B. 2002 Sexual selection fails to promote adaptation to a new environment Yes Yes Yes Yes Yes Also looks at thermal stress
Holland, B. and W. R. Rice 1999 Experimental removal of sexual selection reverses intersexual antagonistic coevolution and removes a reproductive load Yes Yes Yes Yes Yes
Hollis, B., J. L. Fierst and D. Houle 2009 Sexual Selection Accelerates the Elimination of a Deleterious Mutant in Drosophila Melanogaster Yes Yes Yes Yes Yes looked at the purging of a deleterious allele
Hollis, B. and D. Houle 2011 Populations with elevated mutation load do not benefit from the operation of sexual selection Yes Yes Yes Yes Yes Mutagenesis took place and direct fitness measurements were made
Hollis, B., D. Houle and T. J. Kawecki 2016 Evolution of reduced post-copulatory molecular interactions in Drosophila populations lacking sperm competition Yes Yes Yes No No 4 Seminal fluid proteins have a fitness advantage in a polygamous setting, thus is favoured; perhaps this was a bit too ambiguous.
Hollis, B., D. Houle, Z. Yan, T. J. Kawecki and L. Keller 2014 Evolution under monogamy feminizes gene expression in Drosophila melanogaster Yes Yes Yes No No 4 Sex biased gene expression was measured, showing sexual antagonism. Would be stretched to consider it as a fitness measure.
Hollis, B. and T. J. Kawecki 2014 Male cognitive performance declines in the absence of sexual selection Yes Yes Yes Yes Yes Cognitive ability measured in both male and female
Hollis, B., L. Keller and T. J. Kawecki 2017 Sexual selection shapes development and maturation rates in Drosophila Yes Yes Yes Yes Yes Development and fitness measured
Hosken, D. J., O. Y. Martin, S. Wigby, T. Chapman and D. J. Hodgson 2009 Sexual conflict and reproductive isolation in flies Yes Yes Yes No No 4 Reproductive isolation measured without fitness components
House, C. M., Z. Lewis, D. J. Hodgson, N. Wedell, M. D. Sharma, J. Hunt and D. J. Hosken 2013 Sexual and Natural Selection Both Influence Male Genital Evolution Yes Yes Yes No No 4 Genitalia too complicated and hard to extract effect size
Hunt, J., R. R. Snook, C. Mitchell, H. S. Crudgington and A. J. Moore 2012 Sexual selection and experimental evolution of chemical signals in Drosophila pseudoobscura Yes Yes Yes No No 4 Body size measured as well as CHC, like other studies may confer fitness advantage
Immonen, E., R. R. Snook and M. G. Ritchie 2014 Mating system variation drives rapid evolution of the female transcriptome in Drosophila pseudoobscura Yes Yes Yes Yes Yes While transcriptome outcomes not exclusively measuring fitness they also measures aspects of fecundity
Innocenti, P., I. Flis and E. H. Morrow 2014 Female responses to experimental removal of sexual selection components in Drosophila melanogaster Yes Yes Yes Yes Yes To some extent the nature of SS treatment is unclear. Gene expression and fecundity are measured
Jacomb, F., J. Marsh and L. Holman 2016 Sexual selection expedites the evolution of pesticide resistance Yes Yes Yes Yes Yes Pesticide Resistance as an environmental condition that needs to be adapted to
Janicke, T., P. Sandner, S. A. Ramm, D. B. Vizoso and L. Schaerer 2016 Experimentally evolved and phenotypically plastic responses to enforced monogamy in a hermaphroditic flatworm Yes No No 2b Hermaphroditic
Jarzebowska, M. and J. Radwan 2010 Sexual Selection Counteracts Extinction of Small Populations of the Bulb Mites Yes Yes Yes Yes Yes Direct fitness measurements over several generations
Klemme, I. and R. C. Firman 2013 Male house mice that have evolved with sperm competition have increased mating duration and paternity success Yes Yes Yes Yes Yes Paternity Success measured
Long, T. A. F., A. F. Agrawal and L. Rowe 2012 The Effect of Sexual Selection on Offspring Fitness Depends on the Nature of Genetic Variation Yes Yes No No 3 No enforced SS regimes
Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage 2015 Sexual selection protects against extinction Yes Yes Yes Yes Yes Reproductive fitness and time to extinction measured
MacLellan, K., L. Kwan, M. C. Whitlock and H. D. Rundle 2012 Dietary stress does not strengthen selection against single deleterious mutations in Drosophila melanogaster No No 1 Selection based experiment rather than experimental evolution
MacLellan, K., M. C. Whitlock and H. D. Rundle 2009 Sexual selection against deleterious mutations via variable male search success No No 1 Selection based experiment rather than experimental evolution
Maklakov, A. A., R. Bonduriansky and R. C. Brooks 2009 Sex Differences, Sexual Selection, and Ageing: An Experimental Evolution Approach Yes Yes Yes Yes Yes Life History traits of ageing were measured
Maklakov, A. A. and C. Fricke 2009 Sexual selection did not contribute to the evolution of male lifespan under curtailed age at reproduction in a seed beetle Yes Yes Yes No No 4 Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard
Maklakov, A. A., C. Fricke and G. Arnqvist 2007 Sexual selection affects lifespan and aging in the seed beetle Yes Yes Yes No No 4 Pseudoreplication to the above studies mut outcome metrics align less with the meta-analysis so we discard
Mallet, M. A., J. M. Bouchard, C. M. Kimber and A. K. Chippindale 2011 Experimental mutation-accumulation on the X chromosome of Drosophila melanogaster reveals stronger selection on males than females Yes Yes No No 3 No SS+ and SS- treatments
Mallet, M. A. and A. K. Chippindale 2011 Inbreeding reveals stronger net selection on Drosophila melanogaster males: implications for mutation load and the fitness of sexual females No No 1 Mutation levels analysed
Martin, O. Y. and D. J. Hosken 2003 Costs and benefits of evolving under experimentally enforced polyandry or monogamy Yes Yes Yes Yes Yes Crossing took place after Gen 29, results still contain fitness components though
Martin, O. Y. and D. J. Hosken 2004 Reproductive consequences of population divergence through sexual conflict Yes Yes Yes Yes Yes Crossing also took place, it should still be fine as they some populations were not crossed
Matsuyama, T. and H. Kuba 2009 Mating time and call frequency of males between mass-reared and wild strains of melon fly, Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) Yes Yes No No 3 Mate choice in different populations
McGuigan, K., D. Petfield and M. W. Blows 2011 REDUCING MUTATION LOAD THROUGH SEXUAL SELECTION ON MALES Yes Yes Yes Yes Yes The control line was not enforced monomagous (did not remove SS)., it was just a control where the population was mutagenised. No clear SS treatment as level of selection varied across the generations.
McKean, K. A. and L. Nunney 2008 Sexual selection and immune function in Drosophila melanogaster Yes Yes Yes Yes Yes The control line was a 1:1 SR but not enforced monogamy
McLain, D. K. 1992 Population density and the intensity of sexual selection on body length in spatially or temporally restricted natural populations of a seed bug No No 1 Field study
McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 Male-biased sex ratio does not promote increased sperm competitiveness in the seed beetle, Callosobruchus maculatus Yes Yes Yes Yes Yes No SS- (enforced monogamy) just altered SR
McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 A test of the sexy-sperm and good-sperm hypotheses for the evolution of polyandry Yes Yes Yes Yes Yes Polygamy was still randomly done meaning post-cop SS is only available. Numorous measures of fitness conducted
Meffert, L. M., J. L. Regan, S. K. Hicks, N. Mukana and S. B. Day 2006 Testing alternative methods for purging genetic load using the housefly (Musca domestica L.) Yes Yes No No 3 No tsts of SS
Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson, T. Chapman and M. J. G. Gage 2011 Inbreeding Promotes Female Promiscuity Yes Yes No No 3 It does not appear the SS regimes were enforced (fig 1)
Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Experimental Evolution Exposes Female and Male Responses to Sexual Selection and Conflict in Tribolium Castaneum Yes Yes Yes Yes Yes No enforced monogamy (no SS-), but different OSR
Morrow, E. H., A. D. Stewart and W. R. Rice 2008 Assessing the extent of genome-wide intralocus sexual conflict via experimentally enforced gender-limited selection Yes Yes No No 3 Not using different SS treatment lines
Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Sperm Competitive Ability Evolves in Response to Experimental Alteration of Operational Sex Ratio Yes Yes Yes Yes Yes Use an OSR of male and female bias
Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Experimental Evolution of Female Traits under Different Levels of Intersexual Conflict in Drosophila Melanogaster Yes Yes No Yes Yes Use an OSR of male and female bias
Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Rapid adaptation to mammalian sociality via sexually selected traits Yes Yes Yes Yes Yes 3 generations in mice with direct fitness outcomes
Nie, H. and K. Kaneshiro 2016 Sexual selection and incipient speciation in Hawaiian Drosophila No No 1 Artificial selection was conducted alongside mate choice
Palopoli, M. F., C. Peden, C. Woo, K. Akiha, M. Ary, L. Cruze, J. L. Anderson and P. C. Phillips 2015 Natural and experimental evolution of sexual conflict within Caenorhabditis nematodes Yes No No 2b Hermaphroditic, also competition not SS was modulated
Partridge, L. 1980 Mate Choice Increases a Component of Offspring Fitness in Fruit-Flies Yes Yes Yes Yes Yes Competitive success from 1 generation of populations with and without mate choice
Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 The effects of sexual selection on life-history traits: An experimental study on guppies Yes Yes Yes Yes Yes Direct and indirect outcomes
Perry, J. C., R. Joag, D. J. Hosken, N. Wedell, J. Radwan and S. Wigby 2016 Experimental evolution under hyper-promiscuity in Drosophila melanogaster Yes Yes No No 3 SS was manipulated with sex peptide receptor (SPR) not enforced selection conditions
Pischedda, A. and A. Chippindale 2005 Sex, mutation and fitness: asymmetric costs and routes to recovery through compensatory evolution No No 1 Measures the effect of mutation in different populations
Pischedda, A. and A. K. Chippindale 2006 Intralocus sexual conflict diminishes the benefits of sexual selection No No 1 Focussed on fitness effects of conflict, not experimental evolution
Pitnick, S., W. D. Brown and G. T. Miller 2001 Evolution of female remating behaviour following experimental removal of sexual selection Yes Yes Yes Yes Yes Body size and number of progeny measured. Not purpose of study though
Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Males’ evolutionary responses to experimental removal of sexual selection Yes Yes Yes Yes Yes Male and population fitness outcomes measured
Plesnar, A., M. Konior and J. Radwan 2011 The role of sexual selection in purging the genome of induced mutations in the bulb mite (Rizoglyphus robini) Yes Yes Yes Yes Yes
Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop, M. Kolasa, M. Dzialo and J. Radwan 2013 No Evidence for Reproductive Isolation through Sexual Conflict in the Bulb Mite Rhizoglyphus robini Yes Yes Yes No No 4 Reproductive isolation measured without fitness components
Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Mating system affects population performance and extinction risk under environmental challenge Yes Yes Yes Yes Yes
Power, D. J. and L. Holman 2014 Polyandrous females found fitter populations Yes Yes Yes Yes Yes Remating was presented to the females 72 hours after first mating. Measuring effects of polyandry, thus multiple mating has more of an effect. Post copulatory selection will take place though.
Power, D. J. and L. Holman 2015 Assessing the alignment of sexual and natural selection using radiomutagenized seed beetles Yes Yes Yes Yes Yes Experiment 2 Measures affect of SS
Price, T. A. R., G. D. D. Hurst and N. Wedell 2010 Polyandry Prevents Extinction Yes Yes No No 3 Appears that individuals that only mated once still had a choice, post cop SS would be enacted then. Interested in mating freq over choice
Prokop, Z. M., M. A. Prus, T. S. Gaczorek, K. Sychta, J. K. Palka, A. Plesnar-Bielak and M. Skarbon 2017 Do males pay for sex? Sex-specific selection coefficients suggest not No No 1 SS was estimated using models: not enforced in experimental evolution
Promislow, D. E. L., E. A. Smith and L. Pearse 1998 Adult fitness consequences of sexual selection in Drosophila melanogaster Yes Yes Yes Yes Yes
Radwan, J. 2004 Effectiveness of sexual selection in removing mutations induced with ionizing radiation Yes Yes Yes Yes Yes Fitness outcomes measured
Radwan, J., J. Unrug, K. Snigorska and K. Gawronska 2004 Effectiveness of sexual selection in preventing fitness deterioration in bulb mite populations under relaxed natural selection Yes Yes Yes Yes Yes Fitness outcomes measured
Rundle, H. D., S. F. Chenoweth and M. W. Blows 2006 The roles of natural and sexual selection during adaptation to a novel environment Yes Yes Yes Yes Yes Fitness outcomes measured
Rundle, H. D., S. F. Chenoweth and M. W. Blows 2009 The diversification of mate preferences by natural and sexual selection Yes Yes Yes No No 4 CHCs / mate preference outcome measured alongside natural selection. CHCs not used in this meta-analysis
Rundle, H. D., A. Odeen and A. O. Mooers 2007 An experimental test for indirect benefits in Drosophila melanogaster Yes Yes No No 3 Between studs and duds not SS+ / SS-
Savic Veselinovic, M., S. Pavkovic-Lucic, Z. Kurbalija Novicic, M. Jelic and M. Andelkovic 2013 Sexual Selection Can Reduce Mutational Load in Drosophila Subobscura Yes Yes Yes Yes No Data not suitable Irradiated and direct fitness outcomes measured: However when extracting data there were no sample sizes presented so we excluded the study as author did not respond to email
Seslija, D., I. Marecko and N. Tucic 2008 Sexual selection and senescence: Do seed beetle males (Acanthoscelides obtectus, Bruchidae, Coleoptera) shape the longevity of their mates? Yes Yes No No 3 While there is monoandrous lines, these lines were not enforced and choice still existed. Put post-cop choice may be stronger in other lines. This is a strange setup and may be hard to compare with other studies
Sharma, M. D., J. Hunt and D. J. Hosken 2012 Antagonistic Responses to Natural and Sexual Selection and the Sex-Specific Evolution of Cuticular Hydrocarbons in Drosophila Simulans Yes Yes Yes No No 4 CHCs / mate preference outcome measured alongside natural selection
Sharp, N. P. and A. F. Agrawal 2008 Mating density and the strength of sexual selection against deleterious alleles in Drosophila melanogaster No No 1 One generation w/ gene freq. Also no enforced monogamy
Sharp, N. P. and A. F. Agrawal 2009 Sexual Selection and the Random Union of Gametes: Testing for a Correlation in Fitness between Mates in Drosophila melanogaster No No 1 Assortive mating study
Simmons, L. W. and R. C. Firman 2014 Experimental Evidence for the Evolution of the Mammalian Baculum by Sexual Selection Yes Yes Yes No No 4 States that “Far less is known of the fitness consequences of variation in baculum morphology for mammals.” - No direct link with fitness advantage. Genital morphology not used in this meta-analysis
Simmons, L. W. and F. Garcia-Gonzalez 2008 Evolutionary Reduction in Testes Size and Competitive Fertilization Success in Response to the Experimental Removal of Sexual Selection in Dung Beetles Yes Yes Yes Yes Yes Fitness outcomes measured
Simmons, L. W. and F. Garcia-Gonzalez 2011 Experimental coevolution of male and female genital morphology Yes Yes Yes No No 4 Genital morphology has conflicting fitness outcomes for males and females, not used in this meta-analysis
Simmons, L. W., C. M. House, J. Hunt and F. Garcia-Gonzalez 2009 Evolutionary Response to Sexual Selection in Male Genital Morphology Yes Yes Yes No No 4 Genital Morphology not used in this meta-analysis
Snook, R. R., N. A. Gidaszewski, T. Chapman and L. W. Simmons 2013 Sexual selection and the evolution of secondary sexual traits: sex comb evolution in Drosophila Yes Yes Yes No No 4 In D. pseudo monogamy was enforced. Sex combs are cited as having positive fitness effects at high and low numbers. Would not give an accurate representation of a fitness comparison
Tilszer, M., K. Antoszczyk, N. Salek, E. Zajac and J. Radwan 2006 Evolution under relaxed sexual conflict in the bulb mite Rhizoglyphus robini Yes Yes Yes Yes Yes
van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 Rapid Loss of Behavioral Plasticity and Immunocompetence under Intense Sexual Selection Yes Yes Yes Yes Yes Did not use enforced monogamy but had different operational sex ratio
Whitlock, M. C. and D. Bourguet 2000 Factors affecting the genetic load in Drosophila: Synergistic epistasis and correlations among fitness components Yes Yes No No 3 No manipulation of sexual selection
Wigby, S. and T. Chapman 2004 Female resistance to male harm evolves in response to manipulation of sexual conflict Yes Yes Yes Yes Yes Did not use enforced monogamy but had different sex ratio

Data Extraction and Effect Size Calculation

The rules utilised during the data extraction and effect size calculation were as follows:

  1. Arithmatic means, standard deviations/errors and sample sizes were extracted from a paper, supplementary material or a linked data repository (e.g. Data Dryad). This was possible when means and SD were reported in text or in a table. We would preferentially extract data for each experimental evolution line/replicat/family if possible and only extract data for the final reported generation (which was noted down).

  2. If we could not find the means and SD in text format we used web-plot digitizer (v.3.12) to extract data from graphs.

  3. If means were not reported then we extracted a summary statistic or proportion value, which we could later convert to Hedges g’ using the compute.es package (Re 2013). Summary statistics included F, z, t and chi2. These conversions still required providing sample sizes for each treatment so these needed to be extractable from the study. Some summary statistics were obtained from generalized linear model summary tabels, others from straight forward ANOVAs and then some from more complex analysis such as proportional hazards statistical tests.

  4. We also collected various covariates for some of the studies (Table S3), which are discussed later.


The Effect Size Dataset

Table of Effect Sizes

Table S3: Table of effect sizes included in our meta-analysis. See the text following the table for an explanation of each column.

# Load the data and clean up the variable formats
prelim.data <- read.csv('data/Preliminary data frame 22.2.18.csv')
prelim.data$Study.ID <- prelim.data$Study.ID %>% factor()
prelim.data$Group.ID <- prelim.data$Group.ID %>% factor()
prelim.data$Environment <- prelim.data$Environment %>% relevel(ref="Unstressed")
prelim.data$Sex <- prelim.data$Sex %>% relevel(ref="B")

#Outcome.Class.2 is using the categories that were decided by survey. I am keeping both just to check them against each other (how much of a difference it makes)
prelim.data$Outcome.Class <- prelim.data$Outcome.Class %>% relevel(ref="Indirect")
prelim.data$Enforced.Monogamy <- prelim.data$Enforced.Monogamy %>% relevel(ref="NO")
prelim.data$Pre.cop <- prelim.data$Pre.cop %>% factor() %>% relevel(ref="0")
prelim.data$Post.cop <- prelim.data$Post.cop %>% factor() %>% relevel(ref="0")
prelim.data$Blinding <- prelim.data$Blinding %>% factor()

kable(prelim.data, "html") %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "500px")
Study.ID Group.ID Authors Year AuthorYear Species Taxon SS.density.high.to.low SS.ratio.high SS.density.high Pre.cop Post.cop Blinding Generations Enforced.Monogamy n Outcome Sex Ambiguous Outcome.Class Environment g var.g Positive.Fitness mean.low sd.low n.low mean.high sd.high n.high JIF
1 37 Almbro, M. and L. W. Simmons 2014 Almbro 2014 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 3 YES 182 Strength M NO Indirect Stressed 0.385 0.022 1 0.0470000 0.0572364 91 0.0940000 0.1621697 91 4.612
1 37 Almbro, M. and L. W. Simmons 2014 Almbro 2014 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 3 YES 182 Strength M NO Indirect Unstressed 0.000 0.022 1 0.1170000 0.1717091 91 0.1170000 0.1717091 91 4.612
1 37 Almbro, M. and L. W. Simmons 2014 Almbro 2014 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 3 YES 222 Ejaculate Quality and Production M NO Indirect Stressed 0.172 0.018 1 1.8920000 0.9060662 111 2.0510000 0.9376732 111 4.612
1 37 Almbro, M. and L. W. Simmons 2014 Almbro 2014 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 3 YES 222 Ejaculate Quality and Production M NO Indirect Unstressed 0.204 0.018 1 2.1900000 0.9692801 111 2.3820000 0.9060662 111 4.612
1 37 Almbro, M. and L. W. Simmons 2014 Almbro 2014 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 2 YES 414 Reproductive Success F NO Direct Not Stated 0.258 0.010 1 15.4000000 10.0712462 207 18.0000000 10.0712462 207 4.612
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed -0.011 0.010 -1 NA NA NA NA NA NA 4.864
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed 0.434 0.010 -1 NA NA NA NA NA NA 4.864
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed -0.064 0.010 -1 NA NA NA NA NA NA 4.864
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed -0.037 0.010 -1 NA NA NA NA NA NA 4.864
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed -0.129 0.010 -1 NA NA NA NA NA NA 4.864
2 14 Arbuthnott, D. and H. D. Rundle 2012 Arbuthnott 2012 Drosophila melanogaster Fly 60.000 1.00 120.00 1 1 Not Blind 7 YES 400 Mutant Frequency B NO Indirect Stressed 0.032 0.010 -1 NA NA NA NA NA NA 4.864
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Lifespan M NO Indirect Stressed -0.971 0.005 1 30.5200000 5.9396970 450 24.2100000 7.0003571 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Lifespan F NO Indirect Stressed -0.154 0.004 1 34.2800000 26.9407684 450 31.3200000 4.0305087 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Fitness Senescence M NO Indirect Stressed 0.074 0.004 -1 3.6300000 1.2727922 450 3.4300000 3.6062446 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Fitness Senescence F NO Indirect Stressed -0.087 0.004 -1 3.9200000 1.4849242 450 4.3500000 6.7882251 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Offspring Viability M NO Direct Stressed -0.868 0.005 -1 0.0295858 0.0063640 450 0.0372000 0.0106066 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Offspring Viability F NO Direct Stressed -0.148 0.004 -1 0.0264000 0.0254558 450 0.0291000 0.0042426 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Lifespan M NO Indirect Unstressed -0.780 0.005 1 35.5500000 11.0308658 450 26.9400000 11.0308658 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Lifespan F NO Indirect Unstressed -0.146 0.004 1 34.2800000 26.9407684 450 30.1900000 28.8499567 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Fitness Senescence M NO Indirect Unstressed 0.021 0.004 -1 4.5000000 6.1518290 450 4.3300000 9.9702056 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Fitness Senescence F NO Indirect Unstressed -0.038 0.004 -1 4.8200000 5.9396970 450 5.1500000 10.8187337 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Offspring Viability M NO Direct Unstressed -0.855 0.005 -1 0.2580000 0.0042426 450 0.0339000 0.0127279 450 5.210
3 35 Archer, C. R., E. Duffy, D. J. Hosken, M. Mokkonen, K. Okada, K. Oku, M. D. Sharma and J. Hunt 2015 Archer 2015 Drosophila simulans Fly 2.500 4.00 5.00 1 1 Not Blind 45 YES 900 Offspring Viability F NO Direct Unstressed -0.176 0.004 -1 0.0267000 0.0169706 450 0.0305000 0.0254558 450 5.210
5 1 Bernasconi, G. and L. Keller 2001 Bernasconi 2001 Tribolium castaneum Beetle 2.000 3.00 4.00 0 1 Not Blind 3 YES 20 Reproductive Success M NO Direct Unstressed 1.533 0.242 1 0.5600000 0.3600000 10 0.9700000 0.0400000 10 2.673
5 1 Bernasconi, G. and L. Keller 2001 Bernasconi 2001 Tribolium castaneum Beetle 2.000 3.00 4.00 0 1 Not Blind 3 YES 20 Reproductive Success F NO Direct Unstressed -0.123 0.184 1 63.0000000 27.0000000 10 60.0000000 19.0000000 10 2.673
6 15 Brommer, J. E., C. Fricke, D. A. Edward and T. Chapman 2012 Brommer 2012 Drosophila melanogaster Fly 1.500 2.00 3.00 0 1 Not Blind 4 YES 93 Reproductive Success B NO Direct Unstressed -0.378 0.043 1 1.0000000 0.3316625 44 0.8700000 0.3500000 49 4.864
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 21 YES 200 Reproductive Success F NO Direct Stressed -0.216 0.020 1 76.9000000 47.0000000 100 66.4000000 50.0000000 100 4.464
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 28 YES 200 Reproductive Success F NO Direct Stressed 0.280 0.020 1 120.6000000 119.0000000 100 153.6000000 116.0000000 100 4.464
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 28 YES 200 Offspring Viability F NO Direct Stressed 0.365 0.045 1 0.7810000 0.0700000 100 0.8740000 0.0500000 100 4.464
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 21 YES 200 Reproductive Success F NO Direct Unstressed -0.244 0.020 1 108.5000000 44.0000000 100 97.9000000 43.0000000 100 4.464
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 28 YES 200 Reproductive Success F NO Direct Unstressed 0.281 0.020 1 164.1000000 119.0000000 100 197.5000000 119.0000000 100 4.464
7 29 Crudgington, H. S., A. P. Beckerman, L. Br_stle, K. Green and R. R. Snook 2005 Crudgington 2005 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 28 YES 200 Offspring Viability F NO Direct Unstressed -0.311 0.155 1 0.9680000 0.0400000 100 0.9450000 0.0400000 100 4.464
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 62 YES 10 Mating Success M NO Indirect Unstressed -0.168 0.184 1 15.7249071 1.9984654 10 15.3903346 1.7633519 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 61 YES 10 Mating Success M NO Indirect Unstressed -0.576 0.192 1 15.3903346 2.1160222 10 14.3122677 1.4106815 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 60 YES 10 Mating Success M NO Indirect Unstressed 1.311 0.226 1 15.0185874 1.0580111 10 16.3940520 0.9404543 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 58 YES 10 Mating Success M NO Indirect Unstressed 0.512 0.190 1 15.7992565 1.6457951 10 16.6542751 1.5282383 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 62 YES 10 Mating Success M NO Indirect Unstressed 1.373 0.231 1 15.7249071 1.9984654 10 18.0669145 1.1755679 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 61 YES 10 Mating Success M NO Indirect Unstressed 1.190 0.219 1 15.3903346 2.1160222 10 17.6208178 1.4106815 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 60 YES 10 Mating Success M NO Indirect Unstressed 1.305 0.226 1 15.0185874 1.0580111 10 17.1003718 1.8809086 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 58 YES 10 Mating Success M NO Indirect Unstressed 1.928 0.276 1 15.7992565 1.6457951 10 18.5873606 1.0580111 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 62 NO 10 Mating Success M NO Indirect Unstressed 1.713 0.257 1 15.3903346 1.7633519 10 18.0700000 1.1755679 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 61 NO 10 Mating Success M NO Indirect Unstressed 2.248 0.310 1 14.3122677 1.4106815 10 17.6200000 1.4106815 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 60 NO 10 Mating Success M NO Indirect Unstressed 0.458 0.189 1 16.3940520 0.9404543 10 17.1000000 1.8809086 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 58 NO 10 Mating Success M NO Indirect Unstressed 1.414 0.233 1 16.6542751 1.5282383 10 18.5900000 1.0580111 10 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 60 YES 20 Reproductive Success M NO Direct Unstressed 0.060 0.096 1 622.3853211 367.6177813 20 642.9357798 301.9717489 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 59 YES 20 Reproductive Success M NO Direct Unstressed -0.089 0.096 1 760.3669725 407.0054007 20 733.9449541 354.4885748 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 57 YES 20 Reproductive Success M NO Direct Unstressed -0.214 0.097 1 728.0733945 407.0054007 20 648.8073394 315.1009554 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 60 YES 20 Reproductive Success M NO Direct Unstressed 0.515 0.099 1 622.4000000 367.6177800 20 819.0825688 380.7469877 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 59 YES 20 Reproductive Success M NO Direct Unstressed 0.768 0.103 1 760.4000000 407.0054000 20 1200.7339450 682.7187366 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 57 YES 20 Reproductive Success M NO Direct Unstressed 1.068 0.110 1 728.1000000 407.0054000 20 1150.8256880 367.6177813 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 60 NO 20 Reproductive Success M NO Direct Unstressed 0.503 0.099 1 642.9357798 301.9717489 20 819.0825688 380.7469877 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 59 NO 20 Reproductive Success M NO Direct Unstressed 0.841 0.105 1 733.9449541 354.4885748 20 1200.7339450 682.7187366 20 5.429
8 29 Crudgington, H. S., S. Fellows, N. S. Badcock and R. R. Snook 2009 Crudgington 2009 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 57 NO 20 Reproductive Success M NO Direct Unstressed 1.437 0.122 1 648.8073394 315.1009554 20 1150.8256880 367.6177813 20 5.429
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 55 YES 18 Early Fecundity F YES Ambiguous Unstressed -0.861 0.111 1 237.3000000 55.0072700 20 169.5000000 95.8836795 18 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 54 YES 18 Early Fecundity F YES Ambiguous Unstressed -0.655 0.118 1 210.6000000 67.6189300 17 170.5000000 50.7141992 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 55 YES 18 Early Fecundity F YES Ambiguous Unstressed 0.026 0.123 1 0.5230000 0.1833600 20 0.5350000 0.2460732 18 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 54 YES 18 Early Fecundity F YES Ambiguous Unstressed 0.360 0.140 1 0.4960000 0.1772900 17 0.6590000 0.2680019 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 55 YES 18 Early Fecundity F YES Ambiguous Unstressed -1.447 0.132 1 237.3000000 55.0072700 20 150.0000000 63.4958266 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 54 YES 18 Early Fecundity F YES Ambiguous Unstressed -0.739 0.114 1 210.6000000 67.6189300 17 154.1000000 80.6396305 19 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 55 YES 18 Early Fecundity F YES Ambiguous Unstressed 0.620 0.160 1 0.5230000 0.1833600 20 0.7750000 0.2185246 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 54 YES 18 Early Fecundity F YES Ambiguous Unstressed 0.450 0.140 1 0.4960000 0.1772900 17 0.6930000 0.2310216 19 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 55 NO 18 Early Fecundity F YES Ambiguous Unstressed -0.233 0.110 1 169.5000000 95.8836795 18 150.0000000 63.4958266 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 54 NO 18 Early Fecundity F YES Ambiguous Unstressed -0.235 0.107 1 170.5000000 50.7141992 17 154.1000000 80.6396305 19 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 55 NO 18 Early Fecundity F YES Ambiguous Unstressed 0.590 0.160 1 0.5350000 0.2460732 18 0.7750000 0.2185246 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 54 NO 18 Early Fecundity F YES Ambiguous Unstressed 0.080 0.150 1 0.6590000 0.2680019 17 0.6930000 0.2310216 19 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 55 YES 18 Reproductive Success F NO Direct Unstressed -0.520 0.105 1 500.3000000 174.4133000 20 403.8000000 261.3466663 18 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 2.000 3.00 4.00 1 1 Not Blind 54 YES 18 Reproductive Success F NO Direct Unstressed -0.843 0.123 1 474.7000000 195.0229000 17 315.4000000 173.5827468 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 55 YES 18 Reproductive Success F NO Direct Unstressed -2.487 0.188 1 403.8000000 261.3466663 18 228.1000000 152.5549081 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 54 YES 18 Reproductive Success F NO Direct Unstressed -1.065 0.122 1 315.4000000 173.5827468 17 266.1000000 188.3044344 19 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 55 NO 18 Reproductive Success F NO Direct Unstressed -0.796 0.118 1 500.3000000 174.4133000 20 228.1000000 152.5549081 17 3.636
9 29 Crudgington, H. S., S. Fellows and R. R. Snook 2010 Crudgington 2010 Drosophila pseudoobscura Fly 1.750 6.00 7.00 1 1 Not Blind 54 NO 18 Reproductive Success F NO Direct Unstressed -0.266 0.108 1 474.7000000 195.0229000 17 266.1000000 188.3044344 19 3.636
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Body Size M YES Ambiguous Unstressed 0.555 0.002 1 2.2200000 0.0730000 1019 2.2600000 0.0710000 1019 2.792
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Body Size F YES Ambiguous Unstressed 0.111 0.002 1 2.4500000 0.0820000 1019 2.4600000 0.0980000 1019 2.792
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Mating Success M NO Indirect Unstressed -0.663 0.004 1 NA NA NA NA NA NA 2.792
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Mating Success M NO Indirect Unstressed -0.655 0.004 1 NA NA NA NA NA NA 2.792
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Mating Latency M YES Indirect Unstressed -0.197 0.002 -1 126.5000000 15.8000000 1019 129.4000000 13.5000000 1019 2.792
10 29 Debelle, A., M. G. Ritchie and R. R. Snook 2016 Debelle 2016 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 98 YES 2038 Mating Latency M YES Indirect Unstressed 2.486 0.003 -1 153.8000000 19.7000000 1019 113.6000000 11.6000000 1019 2.792
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 38 Reproductive Success F NO Direct Stressed 1.810 0.144 1 91.7000000 9.4400000 19 105.7700000 5.1700000 19 2.606
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 38 Reproductive Success F NO Direct Unstressed 0.299 0.102 1 93.9700000 21.3500000 19 101.2400000 26.0600000 19 2.606
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 24 Reproductive Success M NO Direct Unstressed 0.222 0.156 1 106.8500000 6.2000000 12 108.6500000 9.2000000 12 2.606
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 24 Reproductive Success M NO Direct Unstressed 0.279 0.209 1 0.3000000 0.0500000 12 0.4200000 0.0500000 12 2.606
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 44 Offspring Viability F NO Direct Unstressed 0.415 0.090 1 24.0000000 8.9442719 20 27.0000000 4.8989795 24 2.606
11 2 Demont, M., V. M. Grazer, L. Michalczyk, A. L. Millard, S. H. Sbilordo, B. C. Emerson, M. J. G. Gage and O. Y. Martin 2014 Demont 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 36 YES 45 Offspring Viability M NO Direct Unstressed 0.407 0.088 1 23.0000000 9.3808315 22 26.0000000 4.7958315 23 2.606
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Mating Latency M NO Indirect Stressed -0.324 0.020 1 6.5230000 5.4190000 102 5.0170000 3.6600000 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Mating Duration M NO Ambiguous Stressed 0.219 0.020 1 11.9500000 2.9810000 102 12.6440000 3.3310000 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Mating Latency M NO Indirect Unstressed 0.099 0.019 1 5.5121951 3.5893711 102 5.8885017 3.9412702 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Mating Duration M NO Ambiguous Unstressed 0.393 0.020 1 9.1892361 2.5424101 102 10.4565972 3.7697805 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success F NO Direct Stressed 0.070 0.019 1 72.3810000 35.0550000 102 74.8857645 35.9428779 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success F NO Direct Stressed 0.015 0.019 1 0.6410000 0.5090000 102 0.6491071 0.5545710 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success M NO Direct Stressed 0.001 0.019 1 0.7750000 0.6600000 102 0.7759516 0.7391160 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success F NO Direct Unstressed -0.312 0.020 1 81.9595782 34.6116602 102 71.0632689 35.0553994 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success F NO Direct Unstressed 0.099 0.019 1 0.5946429 0.5004665 102 0.6446429 0.5049752 102 8.090
12 16 Edward, D. A., C. Fricke and T. Chapman 2010 Edward 2010 Drosophila melanogaster Fly 0.760 75.00 76.00 1 1 Not Blind 70 NO 204 Reproductive Success M NO Direct Unstressed 0.049 0.019 1 0.7157439 0.6919384 102 0.7510381 0.7495999 102 8.090
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success F NO Direct Stressed 0.396 0.080 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success F NO Direct Stressed -1.258 0.114 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success F NO Direct Stressed -0.352 0.076 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success F NO Direct Stressed 1.316 0.146 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success M NO Direct Stressed 1.196 0.132 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success M NO Direct Stressed 1.142 0.104 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success M NO Direct Stressed 0.131 0.072 1 NA NA NA NA NA NA 3.248
13 6 Firman, R. C. 2011 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 16 YES 63 Reproductive Success M NO Direct Stressed 1.747 0.360 1 NA NA NA NA NA NA 3.248
14 6 Firman, R. C. 2014 Firman 2011a Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 25 YES 30 Male Attractiveness M NO Indirect Unstressed -1.177 0.149 1 NA NA NA NA NA NA 3.248
15 6 Firman, R. C., L. Y. Cheam and L. W. Simmons 2011 Firman 2011b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 8 YES 54 Ejaculate Quality and Production M NO Indirect Not Stated 0.303 0.073 1 NA NA NA NA NA NA 3.276
15 6 Firman, R. C., L. Y. Cheam and L. W. Simmons 2011 Firman 2011b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 8 YES 54 Ejaculate Quality and Production M NO Indirect Not Stated 1.844 0.105 1 NA NA NA NA NA NA 3.276
16 6 Firman, R. C., F. Garcia-Gonzalez, E. Thyer, S. Wheeler, Z. Yamin, M. Yuan and L. W. Simmons 2015 Firman 2015 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Blind 18 YES 60 Ejaculate Quality and Production M NO Indirect Not Stated 1.003 0.073 1 0.7010000 0.0492950 30 0.7470000 0.0438178 30 4.007
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 88 Reproductive Success F NO Direct Not Stated -0.963 0.068 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 41 Reproductive Success F NO Direct Not Stated -1.733 0.349 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 78 Reproductive Success F NO Direct Not Stated -1.717 0.111 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 55 Reproductive Success F NO Direct Not Stated -0.974 0.115 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 86 Reproductive Success F NO Direct Not Stated -0.599 0.102 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 55 Reproductive Success F NO Direct Not Stated -0.904 0.159 1 NA NA NA NA NA NA 3.832
17 6 Firman, R. C., M. Gomendio, E. R. S. Roldan and L. W. Simmons 2014 Firman 2014b Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 24 YES 36 Reproductive Success F NO Direct Not Stated -0.504 0.199 1 NA NA NA NA NA NA 3.832
18 6 Firman, R. C. and L. W. Simmons 2010 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 8 YES 144 Ejaculate Quality and Production M NO Indirect Not Stated 0.399 0.026 1 NA NA NA NA NA NA 3.521
18 6 Firman, R. C. and L. W. Simmons 2010 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 7 YES 40 Reproductive Success F NO Direct Stressed -0.564 0.100 1 17.5500000 5.4112845 20 14.4500000 5.3665631 20 3.521
18 6 Firman, R. C. and L. W. Simmons 2010 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 7 YES 40 Reproductive Success F NO Direct Unstressed -0.328 0.097 1 16.1500000 4.1143651 20 14.5500000 5.3665631 20 3.521
18 6 Firman, R. C. and L. W. Simmons 2010 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 10 YES 144 Reproductive Success F NO Direct Not Stated 0.668 0.029 1 NA NA NA NA NA NA 3.521
18 6 Firman, R. C. and L. W. Simmons 2010 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 8 YES 128 Body Size B NO Ambiguous Not Stated -0.364 0.031 1 NA NA NA NA NA NA 3.521
19 6 Firman, R. C. and L. W. Simmons 2011 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 12 YES 128 Reproductive Success M NO Direct Stressed -1.008 0.035 1 NA NA NA NA NA NA 3.521
20 6 Firman, R. C. and L. W. Simmons 2012 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 15 YES 144 Reproductive Success F NO Direct Unstressed 0.784 0.030 1 4.9400000 2.2910260 72 6.6500000 2.0364675 72 3.521
20 6 Firman, R. C. and L. W. Simmons 2012 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 15 YES 128 Reproductive Success F NO Direct Unstressed -0.213 0.031 1 NA NA NA NA NA NA 3.521
20 6 Firman, R. C. and L. W. Simmons 2012 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 15 YES 128 Reproductive Success F NO Direct Unstressed 0.416 0.032 1 NA NA NA NA NA NA 3.521
20 6 Firman, R. C. and L. W. Simmons 2012 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 15 YES 128 Offspring Viability B NO Direct Unstressed 0.014 0.031 1 NA NA NA NA NA NA 3.521
20 6 Firman, R. C. and L. W. Simmons 2012 Firman 2010 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 15 YES 128 Offspring Viability B NO Direct Unstressed 0.408 0.032 1 NA NA NA NA NA NA 3.521
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 155 Body Size F YES Ambiguous Not Stated 0.080 0.026 1 0.0011635 0.0001053 77 0.0011720 0.0001073 78 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 155 Body Size M YES Ambiguous Not Stated 0.102 0.026 1 0.0009178 0.0000963 77 0.0009283 0.0001084 77 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 76 Development Rate B NO Ambiguous Stressed -0.453 0.053 1 0.8289099 0.0286151 38 0.8135570 0.0377825 38 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 79 Development Rate B NO Ambiguous Unstressed 0.772 0.053 1 0.8251609 0.0378719 39 0.8534363 0.0346177 40 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 76 Reproductive Success F NO Direct Stressed -0.579 0.054 1 419.3947368 34.3546995 38 397.4210526 40.5573545 38 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 79 Reproductive Success F NO Direct Unstressed 0.185 0.050 1 292.2051282 55.6900159 39 301.0500000 37.3678526 40 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 76 Offspring Viability F NO Direct Stressed -0.476 0.053 1 0.5462428 0.0780889 38 0.5107408 0.0691831 38 4.502
22 9 Fricke, C. and G. Arnqvist 2007 Fricke 2007 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 35 YES 79 Offspring Viability F NO Direct Unstressed 0.543 0.052 1 0.4180605 0.0784570 39 0.4575765 0.0652579 40 4.502
23 7 Fritzsche, K., I. Booksmythe and G. Arnqvist 2016 Fritzsche 2016 Megabruchidius dorsalis Beetle 1.000 5.00 150.00 1 1 Blind 20 NO 1200 Reproductive Success M NO Direct Not Stated -0.056 0.003 1 NA NA NA NA NA NA 8.851
23 7 Fritzsche, K., I. Booksmythe and G. Arnqvist 2016 Fritzsche 2016 Megabruchidius dorsalis Beetle 1.000 5.00 150.00 1 1 Blind 20 NO 1200 Reproductive Success F NO Direct Not Stated -0.031 0.003 1 NA NA NA NA NA NA 8.851
23 7 Fritzsche, K., I. Booksmythe and G. Arnqvist 2016 Fritzsche 2016 Megabruchidius dorsalis Beetle 1.000 5.00 150.00 1 1 Blind 20 NO 1200 Lifespan M NO Indirect Not Stated -0.066 0.003 1 NA NA NA NA NA NA 8.851
23 7 Fritzsche, K., I. Booksmythe and G. Arnqvist 2016 Fritzsche 2016 Megabruchidius dorsalis Beetle 1.000 5.00 150.00 1 1 Blind 20 NO 1200 Lifespan F NO Indirect Not Stated -0.083 0.003 1 NA NA NA NA NA NA 8.851
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 90 Ejaculate Quality and Production M NO Indirect Not Stated -0.197 0.044 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 256 Mating Success M NO Indirect Not Stated -0.041 0.016 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 256 Mating Success M NO Indirect Not Stated -0.065 0.016 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 256 Mating Success M NO Indirect Not Stated -0.078 0.016 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 256 Mating Success M NO Indirect Not Stated -0.267 0.016 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 184 Reproductive Success B NO Direct Not Stated 0.095 0.022 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 184 Reproductive Success B NO Direct Not Stated 0.407 0.022 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 392 Reproductive Success B NO Direct Not Stated 0.059 0.010 1 NA NA NA NA NA NA 5.051
24 30 Fritzsche, K., N. Timmermeyer, M. Wolter and N. K. Michiels 2014 Fritzsche 2014 Caenorhabditis remanei Nematode 1.000 5.00 60.00 0 0 Not Blind 20 NO 392 Reproductive Success B NO Direct Not Stated 0.219 0.010 1 NA NA NA NA NA NA 5.051
26 10 Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady 2009 Gay 2009 Callosobruchus maculatus Beetle 60.000 1.00 120.00 1 1 Not Blind 90 YES 80 Body Size M YES Ambiguous Unstressed 1.971 0.073 1 1.8700000 0.0822192 40 2.0400000 0.0885438 40 3.816
26 10 Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady 2009 Gay 2009 Callosobruchus maculatus Beetle 60.000 1.00 120.00 1 1 Not Blind 90 YES 80 Ejaculate Quality and Production M NO Indirect Unstressed 1.385 0.061 1 0.4500000 0.1201666 40 0.6400000 0.1517893 40 3.816
26 10 Gay, L., D. J. Hosken, R. Vasudev, T. Tregenza and P. E. Eady 2009 Gay 2009 Callosobruchus maculatus Beetle 60.000 1.00 120.00 1 1 Not Blind 90 YES 80 Ejaculate Quality and Production M NO Indirect Unstressed 0.661 0.052 1 0.1571000 0.0059000 40 0.1626000 0.0103000 40 3.816
27 2 Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin 2014 Grazer 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 39 YES 228 Reproductive Success B NO Direct Stressed 0.211 0.018 1 149.9000000 174.9000000 114 181.6000000 119.5000000 114 3.368
27 2 Grazer, V. M., M. Demont, L. Michalczyk, M. J. G. Gage and O. Y. Martin 2014 Grazer 2014 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 39 YES 240 Reproductive Success B NO Direct Unstressed 0.214 0.017 1 240.6000000 189.7000000 120 291.5000000 275.6000000 120 3.368
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 66 Immunity M NO Ambiguous Unstressed -0.141 0.059 1 6.9700000 1.7400000 33 6.7000000 2.0300000 33 2.591
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 66 Immunity F NO Ambiguous Unstressed 0.848 0.065 1 6.3300000 1.3400000 33 7.7900000 2.0000000 33 2.591
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 288 Immunity M NO Ambiguous Stressed 0.175 0.014 1 80.8600000 41.5400000 144 87.9200000 39.1000000 144 2.591
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 288 Immunity F NO Ambiguous Stressed 0.089 0.014 1 85.0000000 41.5400000 144 88.9400000 46.4300000 144 2.591
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 288 Immunity M NO Ambiguous Unstressed -0.097 0.014 1 92.8100000 35.8400000 144 89.2100000 38.2800000 144 2.591
28 2 Hangartner, S., L. Michalczyk, M. J. G. Gage and O. Y. Martin 2015 Hangartner 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 49 YES 288 Immunity F NO Ambiguous Unstressed 0.070 0.014 1 87.9900000 35.8400000 144 90.2900000 29.3200000 144 2.591
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity M NO Ambiguous Unstressed -0.107 0.054 1 6.0300000 2.5400000 36 5.7500000 2.6200000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity F NO Ambiguous Unstressed 0.121 0.054 1 6.8100000 2.6200000 36 7.1300000 2.6200000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity B NO Ambiguous Unstressed -0.281 0.055 1 6.8400000 3.6700000 36 5.8200000 3.5000000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 72 Immunity M NO Ambiguous Unstressed -0.043 0.054 1 6.1400000 2.5400000 36 5.7500000 2.6200000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 72 Immunity F NO Ambiguous Unstressed -0.203 0.055 1 7.3400000 2.5400000 36 7.1300000 2.6200000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 72 Immunity B NO Ambiguous Unstressed -0.074 0.054 1 7.1100000 3.5000000 36 5.8200000 3.5000000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity M NO Ambiguous Unstressed -0.150 0.055 1 6.1400000 2.5400000 36 6.0300000 2.5400000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity F NO Ambiguous Unstressed -0.081 0.054 1 7.3400000 2.5400000 36 6.8100000 2.6200000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 72 Immunity B NO Ambiguous Unstressed -0.361 0.394 1 7.1100000 3.5000000 36 6.8400000 3.6700000 36 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Stressed -0.073 0.014 1 1.5000000 2.6800000 144 1.7100000 3.0500000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity F NO Ambiguous Stressed 0.035 0.014 1 1.4600000 2.9600000 144 1.3600000 2.7800000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Unstressed -0.164 0.014 1 2.1100000 3.3300000 144 1.6200000 2.5900000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity F NO Ambiguous Unstressed 0.022 0.014 1 2.6900000 4.3500000 144 2.7900000 4.8100000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Stressed 0.013 0.014 1 1.6700000 2.9600000 144 1.7100000 3.0500000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 288 Immunity F NO Ambiguous Stressed -0.025 0.014 1 1.4300000 2.7800000 144 1.3600000 2.7800000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Unstressed -0.029 0.014 1 2.2100000 3.5200000 144 1.6200000 2.5900000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.000 1.00 100.00 1 1 Not Blind 56 NO 288 Immunity F NO Ambiguous Unstressed 0.068 0.014 1 2.4100000 3.8900000 144 2.7900000 4.8100000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Stressed -0.060 0.014 1 1.6700000 2.9600000 144 1.5000000 2.6800000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity F NO Ambiguous Stressed 0.010 0.014 1 1.4300000 2.7800000 144 1.4600000 2.9600000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 288 Immunity M NO Ambiguous Unstressed -0.190 0.014 1 2.2100000 3.5200000 144 2.1100000 3.3300000 144 3.264
29 2 Hangartner, S., S. H. Sbilordo, _. Michalczyk, M. J. G. Gage and O. Y. Martin 2013 Hangartner 2013 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 56 NO 144 Immunity F NO Ambiguous Unstressed 0.087 0.014 1 2.4100000 3.8900000 144 2.6900000 4.3500000 144 3.264
30 17 Holland, B. 2002 Holland 2002 Drosophila melanogaster Fly 2.500 4.00 5.00 1 1 Not Blind 38 YES 89 Reproductive Success F NO Direct Stressed -0.116 0.015 1 11.5900000 10.1800000 133 10.6600000 4.9500000 133 3.516
30 17 Holland, B. 2002 Holland 2002 Drosophila melanogaster Fly 2.500 4.00 5.00 1 1 Not Blind 51 YES 89 Reproductive Success F NO Direct Stressed 0.070 0.015 1 14.4300000 3.2800000 133 14.8100000 6.9300000 133 3.516
31 18 Holland, B. and W. R. Rice 1999 Holland 1999 Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Not Blind 47 YES 76 Reproductive Success F NO Direct Stressed -0.305 0.018 1 11.2400000 10.6600000 114 8.9300000 3.6000000 114 10.260
32 19 Hollis, B., J. L. Fierst and D. Houle 2009 Hollis 2009 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 8 YES 27 Mutant Frequency M NO Indirect Stressed 0.807 0.053 -1 NA NA NA NA NA NA 5.429
32 19 Hollis, B., J. L. Fierst and D. Houle 2009 Hollis 2009 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 8 YES 27 Mutant Frequency M NO Indirect Unstressed 0.237 0.049 -1 0.9410000 1.7760000 40 0.3990000 2.6590000 40 5.429
33 19 Hollis, B. and D. Houle 2011 Hollis 2011 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 60 YES 120 Reproductive Success B NO Direct Stressed -0.304 0.011 1 126.5900000 28.9794410 180 117.6000000 29.1136051 180 3.276
33 19 Hollis, B. and D. Houle 2011 Hollis 2011 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 60 YES 164 Reproductive Success F NO Direct Stressed 0.031 0.008 1 NA NA NA NA NA NA 3.276
33 19 Hollis, B. and D. Houle 2011 Hollis 2011 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 60 YES 164 Offspring Viability F NO Direct Stressed -0.064 0.008 1 NA NA NA NA NA NA 3.276
34 19 Hollis, B. and T. J. Kawecki 2014 Hollis 2014 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 100 YES 38 Mating Latency M YES Indirect Stressed 0.038 0.062 1 NA NA NA NA NA NA 5.051
34 19 Hollis, B. and T. J. Kawecki 2014 Hollis 2014 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 100 YES 90 Mating Latency M YES Indirect Stressed 0.194 0.043 1 NA NA NA NA NA NA 5.051
34 19 Hollis, B. and T. J. Kawecki 2014 Hollis 2014 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 100 YES 17 Reproductive Success M NO Direct Stressed 1.216 0.091 1 0.6010000 0.2950000 23 0.8760000 0.1380000 28 5.051
34 19 Hollis, B. and T. J. Kawecki 2014 Hollis 2014 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 100 YES 21 Reproductive Success M NO Direct Stressed 0.659 0.066 -1 0.5530000 0.3660000 30 0.7710000 0.2860000 33 5.051
34 19 Hollis, B. and T. J. Kawecki 2014 Hollis 2014 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 100 YES 15 Reproductive Success M NO Direct Stressed 0.830 0.090 -1 0.6100000 0.3400000 22 0.8530000 0.2300000 23 5.051
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 139 YES 48 Development Rate M NO Ambiguous Stressed -0.482 0.028 1 NA NA NA NA NA NA 4.201
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 139 YES 48 Development Rate F NO Ambiguous Stressed 0.414 0.028 1 NA NA NA NA NA NA 4.201
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 162 YES 60 Body Size M YES Ambiguous Stressed 0.000 0.022 1 NA NA NA NA NA NA 4.201
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 162 YES 60 Body Size F YES Ambiguous Stressed -0.238 0.022 1 NA NA NA NA NA NA 4.201
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 117 YES 44 Fitness Senescence M YES Indirect Stressed 0.500 0.031 -1 NA NA NA NA NA NA 4.201
35 19 Hollis, B., L. Keller and T. J. Kawecki 2017 Hollis 2017 Drosophila melanogaster Fly 5.000 1.00 10.00 1 1 Not Blind 117 YES 45 Fitness Senescence F YES Indirect Stressed 0.017 0.030 -1 NA NA NA NA NA NA 4.201
36 29 Immonen, E., R. R. Snook and M. G. Ritchie 2014 Immonen 2014 Drosophila pseudoobscura Fly 3.500 6.00 7.00 1 1 Not Blind 100 YES 30 Reproductive Success F NO Direct Unstressed 0.636 0.046 1 NA NA NA NA NA NA 2.320
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 30 NO 110 Body Size M YES Ambiguous Unstressed -0.306 0.120 1 780.1295325 24.5249313 169 773.1405125 20.8806168 160 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 30 NO 107 Body Size F YES Ambiguous Unstressed -0.290 0.013 1 879.0553188 25.2349182 160 870.6142500 32.5085570 160 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 30 NO 27 Reproductive Success F NO Direct Unstressed 0.745 0.053 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 31 NO 27 Reproductive Success F NO Direct Unstressed 0.490 0.051 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 50 NO 27 Reproductive Success F NO Direct Unstressed 0.545 0.051 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 58 NO 27 Reproductive Success F NO Direct Unstressed 0.379 0.050 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 30 NO 27 Reproductive Success F NO Direct Unstressed -0.228 0.049 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 31 NO 27 Reproductive Success F NO Direct Unstressed 0.300 0.050 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 50 NO 27 Reproductive Success F NO Direct Unstressed -0.108 0.049 1 NA NA NA NA NA NA 3.368
37 20 Innocenti, P., I. Flis and E. H. Morrow 2014 Innocenti 2014 Drosophila melanogaster Fly 1.000 1.00 96.00 0 1 Not Blind 58 NO 27 Reproductive Success F NO Direct Unstressed 0.080 0.049 1 NA NA NA NA NA NA 3.368
38 3 Jacomb, F., J. Marsh and L. Holman 2016 Jacomb 2016 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Blind 5 YES 320 Pesticide Resistance B NO Ambiguous Stressed 1.246 0.005 1 0.8560000 0.0210000 480 0.8920000 0.0350000 480 4.201
38 3 Jacomb, F., J. Marsh and L. Holman 2016 Jacomb 2016 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Blind 5 YES 176 Pesticide Resistance B NO Ambiguous Unstressed 1.001 0.005 1 0.0880000 0.0850000 480 0.0270000 0.0140000 48 4.201
39 32 Jarzebowska, M. and J. Radwan 2010 Jarzebowska 2010 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 8 YES 72 Reproductive Success F NO Direct Stressed 0.390 0.019 -1 0.7390000 0.5240000 96 0.8080000 0.4010000 120 5.659
39 32 Jarzebowska, M. and J. Radwan 2010 Jarzebowska 2010 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 8 YES 72 Reproductive Success F NO Direct Unstressed -0.190 0.020 1 0.8350000 0.4730000 96 0.7880000 0.5730000 120 5.659
39 32 Jarzebowska, M. and J. Radwan 2010 Jarzebowska 2010 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 8 YES 11 Extinction Rate B NO Direct Stressed 0.752 0.133 1 NA NA NA NA NA NA 5.659
39 32 Jarzebowska, M. and J. Radwan 2010 Jarzebowska 2010 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 8 YES 72 Offspring Viability B NO Direct Stressed 0.150 0.019 1 37.9100000 27.9900000 96 51.3200000 38.5200000 120 5.659
39 32 Jarzebowska, M. and J. Radwan 2010 Jarzebowska 2010 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 8 YES 72 Offspring Viability B NO Direct Unstressed -0.088 0.019 1 70.4400000 32.3000000 96 61.8700000 54.1700000 120 5.659
40 6 Klemme, I. and R. C. Firman 2013 Klemme 2013 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 18 YES 12 Reproductive Success M NO Direct Stressed 0.769 0.114 1 0.2800000 0.4200000 18 0.7200000 0.6700000 18 3.068
40 6 Klemme, I. and R. C. Firman 2013 Klemme 2013 Mus domesticus Mouse 2.000 3.00 4.00 0 1 Not Blind 18 YES 12 Reproductive Success M NO Direct Unstressed 0.946 0.119 1 0.3400000 0.3900000 18 0.7900000 0.5300000 18 3.068
41 4 Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage 2015 Lumley 2015 Tribolium castaneum Beetle 1.000 9.00 100.00 1 1 Not Blind 20 NO 56 Reproductive Success B NO Direct Stressed 0.576 0.025 -1 NA NA NA NA NA NA 38.138
41 4 Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage 2015 Lumley 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 20 YES 16 Reproductive Success B NO Direct Stressed 0.559 0.084 -1 NA NA NA NA NA NA 38.138
41 4 Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage 2015 Lumley 2015 Tribolium castaneum Beetle 1.000 9.00 100.00 1 1 Not Blind 20 NO 56 Extinction Rate B NO Direct Stressed 0.522 0.024 1 NA NA NA NA NA NA 38.138
41 4 Lumley, A. J., L. Michalczyk, J. J. N. Kitson, L. G. Spurgin, C. A. Morrison, J. L. Godwin, M. E. Dickinson, O. Y. Martin, B. C. Emerson, T. Chapman and M. J. G. Gage 2015 Lumley 2015 Tribolium castaneum Beetle 3.000 5.00 6.00 1 1 Not Blind 20 YES 16 Extinction Rate B NO Direct Stressed 0.798 0.087 1 NA NA NA NA NA NA 38.138
42 11 Maklakov, A. A., R. Bonduriansky and R. C. Brooks 2009 Maklakov 2009 Callosobruchus maculatus Beetle 50.000 1.00 100.00 1 1 Not Blind 11 YES 11 Reproductive Success F NO Direct Not Stated -0.958 0.133 1 155.1200000 37.7600000 16 118.0000000 37.7600000 16 5.429
43 5 Martin, O. Y. and D. J. Hosken 2003 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 29 YES 10 Lifespan F YES Indirect Unstressed 0.841 0.138 1 NA NA NA NA NA NA 3.833
43 5 Martin, O. Y. and D. J. Hosken 2003 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 29 YES 10 Mating Success M NO Indirect Unstressed 0.920 0.140 1 NA NA NA NA NA NA 3.833
43 5 Martin, O. Y. and D. J. Hosken 2003 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 29 YES 10 Reproductive Success F NO Direct Unstressed 1.038 0.144 1 28.2000000 15.4532035 15 49.2000000 23.1604404 15 3.833
43 5 Martin, O. Y. and D. J. Hosken 2003 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 29 YES 10 Lifespan F NO Indirect Stressed -1.314 0.155 1 2.2130508 0.0600641 15 2.1161864 0.0817265 15 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 42 YES 12 Reproductive Success F NO Direct Unstressed 0.421 0.159 1 34.9043478 21.5075526 12 42.9391304 14.7600851 12 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 250.000 1.00 500.00 1 1 Not Blind 42 YES 12 Reproductive Success F NO Direct Unstressed -0.075 0.155 1 34.9043478 21.5075526 12 33.4434783 15.6035186 12 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 10.000 1.00 500.00 1 1 Not Blind 42 NO 12 Reproductive Success F NO Direct Unstressed -0.603 0.163 1 42.9391304 14.7600851 12 33.4434783 15.6035186 12 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 25.000 1.00 50.00 1 1 Not Blind 42 YES 24 Lifespan F NO Indirect Unstressed -0.405 0.082 1 17.3460898 2.4943223 24 16.3327787 2.4698682 24 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 250.000 1.00 500.00 1 1 Not Blind 42 YES 24 Lifespan F NO Indirect Unstressed -0.638 0.085 1 17.3460898 2.4943223 24 15.8086522 2.2497809 24 3.833
44 5 Martin, O. Y. and D. J. Hosken 2004 Martin 2003 Sepsis cynipsea Fly 10.000 1.00 500.00 1 1 Not Blind 42 NO 24 Lifespan F NO Indirect Unstressed -0.216 0.081 1 16.3327787 2.4698682 24 15.8086522 2.2497809 24 3.833
45 33 McGuigan, K., D. Petfield and M. W. Blows 2011 McGuigan 2011 Drosophila serrata Fly 2.405 3.81 4.81 1 0 Not Blind 23 YES 292 Mating Success M NO Indirect Stressed 0.034 0.014 1 0.4997000 0.3400000 146 0.5097000 0.2460000 146 5.146
45 33 McGuigan, K., D. Petfield and M. W. Blows 2011 McGuigan 2011 Drosophila serrata Fly 2.405 3.81 4.81 1 0 Not Blind 26 YES 208 Reproductive Success F NO Direct Stressed 0.114 0.019 1 49.9300000 22.7000000 104 52.1740000 16.1000000 104 5.146
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 40 Body Size B YES Ambiguous Unstressed 1.528 0.242 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 40 Development Rate B NO Ambiguous Unstressed 0.853 0.105 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 40 Development Rate B NO Ambiguous Unstressed 3.124 0.218 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 40 Development Rate B NO Ambiguous Unstressed 2.655 0.184 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 52 Mating Success M NO Indirect Unstressed 0.839 0.081 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 52 Mating Success M NO Indirect Unstressed 1.598 0.099 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 52 Mating Success M NO Indirect Unstressed 1.907 0.110 1 NA NA NA NA NA NA 4.737
46 21 McKean, K. A. and L. Nunney 2008 McKean 2008 Drosophila melanogaster Fly 1.700 2.40 170.00 1 1 Not Blind 58 NO 80 Immunity B NO Ambiguous Unstressed -0.911 0.054 -1 NA NA NA NA NA NA 4.737
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 153 Ejaculate Quality and Production M YES Indirect Unstressed -0.106 0.027 1 2.7000000 0.8442748 88 2.6000000 1.0480935 65 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 145 Ejaculate Quality and Production M YES Indirect Unstressed -0.157 0.028 1 0.1600000 0.0728835 83 0.1500000 0.0472440 62 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 202 Ejaculate Quality and Production M NO Indirect Unstressed 0.100 0.020 1 0.5700000 0.1014889 103 0.5800000 0.0994987 99 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 101 Ejaculate Quality and Production M YES Indirect Unstressed -0.280 0.039 1 0.8600000 0.1428286 51 0.8200000 0.1414214 50 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 127 Mating Duration M YES Ambiguous Unstressed -0.371 0.032 1 534.6200000 204.1600000 64 466.0200000 160.0944118 63 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 127 Reproductive Success F NO Direct Unstressed 0.156 0.031 1 34.2600000 18.7200000 64 37.1700000 18.2556840 63 4.259
47 28 McNamara, K. B., S. P. Robinson, M. E. Rosa, N. S. Sloan, E. van Lieshout and L. W. Simmons 2016 McNamara 2016 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 32 NO 125 Reproductive Success M NO Direct Unstressed 0.315 0.032 1 0.6700000 0.3149603 62 0.7700000 0.3174902 63 4.259
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 351 Ejaculate Quality and Production M NO Indirect Unstressed 0.568 0.012 1 0.9400000 0.3200000 179 1.0800000 0.1300000 172 3.177
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 336 Immunity M NO Ambiguous Unstressed 0.000 0.012 1 1.6500000 3.4000000 175 1.6500000 3.2000000 161 3.177
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 413 Immunity F NO Ambiguous Unstressed -0.050 0.010 1 80.2000000 21.8000000 203 79.0500000 20.3000000 210 3.177
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 788 Immunity B NO Ambiguous Unstressed -0.106 0.005 -1 NA NA NA NA NA 401 3.177
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 335 Immunity M NO Ambiguous Unstressed -0.108 0.012 1 0.5300000 0.1650000 173 0.5100000 0.2050000 162 3.177
48 12 McNamara, K. B., E. van Lieshout and L. W. Simmons 2014 McNamara 2014 Teleogryllus oceanicus Cricket 2.000 3.00 4.00 0 1 Blind 1 YES 406 Immunity F NO Ambiguous Unstressed -0.098 0.010 1 0.5500000 0.2040000 202 0.5300000 0.2050000 204 3.177
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 47 Mating Latency M YES Indirect Unstressed 0.556 0.086 1 358.9000000 494.4000000 24 143.4000000 203.6000000 23 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 57 Mating Latency M YES Indirect Unstressed 0.470 0.070 1 294.7000000 313.6000000 28 158.0000000 259.1000000 29 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 53 Mating Duration M YES Ambiguous Unstressed 1.987 0.112 1 73.5000000 67.7000000 30 483.4000000 299.5000000 23 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 58 Mating Duration M YES Ambiguous Unstressed 0.551 0.070 1 181.8000000 198.5000000 29 323.3000000 298.6000000 29 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 53 Mating Frequency M YES Indirect Unstressed 1.982 0.112 1 2.1000000 2.2000000 30 22.2000000 15.0000000 23 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 58 Mating Frequency M YES Indirect Unstressed 0.929 0.075 1 4.2000000 4.1000000 29 15.0000000 15.7000000 29 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 30 Reproductive Success F NO Direct Stressed 1.852 0.183 1 183.8000000 80.6000000 15 409.5000000 147.0000000 15 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 30 Reproductive Success F NO Direct Unstressed 0.061 0.126 1 346.1000000 255.8000000 15 366.3000000 378.7000000 15 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 20 Reproductive Success M NO Direct Unstressed 0.614 0.193 1 0.4570000 0.3580000 10 0.6320000 0.1450000 10 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 20 Reproductive Success M NO Direct Unstressed 0.931 0.205 1 0.5290000 0.2500000 10 0.7200000 0.1210000 10 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 20 Reproductive Success M NO Direct Unstressed 0.319 0.186 1 0.5700000 0.0640000 10 0.6210000 0.2070000 10 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed -0.219 0.156 1 0.4530000 0.3920000 12 0.3610000 0.4180000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed 0.178 0.156 1 0.4450000 0.4240000 12 0.5140000 0.3180000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed 0.025 0.155 -1 0.4210000 0.3560000 12 0.4300000 0.3340000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed 0.110 0.156 -1 0.7970000 0.3520000 12 0.8330000 0.2730000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed 0.724 0.166 1 0.6940000 0.3350000 12 0.9010000 0.2000000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 24 Reproductive Success M NO Direct Unstressed -0.389 0.159 1 0.8390000 0.2080000 12 0.7280000 0.3300000 12 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 30 Lifespan F NO Indirect Stressed 0.211 0.127 1 8.3000000 2.5500000 15 8.9000000 2.9700000 15 5.146
49 4 Michalczyk, L., A. L. Millard, O. Y. Martin, A. J. Lumley, B. C. Emerson and M. J. G. Gage 2011 Michalczyk 2011 Tribolium castaneum Beetle 1.050 6.00 105.00 1 1 Not Blind 20 NO 29 Lifespan F NO Indirect Unstressed 0.677 0.138 1 8.8000000 2.7200000 14 10.3000000 1.4400000 15 5.146
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 27 Mating Success M NO Indirect Unstressed 0.657 0.076 1 0.2000000 0.1120000 27 0.2540000 0.1140000 27 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 29 Mating Success M NO Indirect Unstressed 0.041 0.069 1 0.8890000 0.0740000 28 0.8920000 0.0700000 30 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 31 Mating Success M NO Indirect Unstressed 0.211 0.063 1 0.8760000 0.1030000 31 0.9010000 0.1290000 31 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 145 Mating Latency M YES Indirect Unstressed -0.062 0.014 1 2.9400000 1.8800000 149 3.1200000 3.6900000 143 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 145 Mating Duration M YES Ambiguous Unstressed -0.918 0.015 -1 11.7400000 2.3700000 149 14.0500000 2.6500000 143 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 122 Mating Success M NO Indirect Unstressed 0.153 0.016 -1 0.0771000 0.1630000 122 0.1023000 0.1650000 121 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 27 Mating Success M NO Indirect Unstressed 0.314 0.073 1 0.1700000 0.0720000 27 0.2000000 0.1120000 27 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 29 Mating Success M NO Indirect Unstressed 0.613 0.073 1 0.8350000 0.0980000 28 0.8890000 0.0740000 28 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 31 Mating Success M NO Indirect Unstressed -0.178 0.064 1 0.8970000 0.1290000 30 0.8760000 0.1030000 31 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 145 Mating Latency M YES Indirect Unstressed 0.159 0.014 1 3.5600000 5.2200000 142 2.9400000 1.8800000 149 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 145 Mating Duration M YES Ambiguous Unstressed 0.471 0.014 1 12.8800000 2.4600000 142 11.7400000 2.3700000 149 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 122 Mating Success M NO Indirect Unstressed 0.170 0.016 -1 0.0543000 0.0961000 122 0.0771000 0.1630000 122 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 27 Mating Success M NO Indirect Unstressed 0.868 0.079 1 0.1700000 0.0720000 27 0.2540000 0.1140000 27 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 29 Mating Success M NO Indirect Unstressed 0.660 0.073 1 0.8350000 0.0980000 28 0.8920000 0.0700000 30 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 31 Mating Success M NO Indirect Unstressed 0.031 0.064 1 0.8970000 0.1290000 30 0.9010000 0.1290000 31 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 145 Mating Latency M YES Indirect Unstressed 0.097 0.014 1 3.5600000 5.2200000 142 3.1200000 3.6900000 143 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 145 Mating Duration M YES Ambiguous Unstressed -0.456 0.014 -1 12.8800000 2.4600000 142 14.0500000 2.6500000 143 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 122 Mating Success M NO Indirect Unstressed 0.355 0.017 -1 0.0543000 0.0961000 122 0.1023000 0.1650000 121 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 1440 Offspring Viability B NO Direct Unstressed 0.088 0.001 1 0.8700000 0.3415260 1440 0.9000000 0.3415260 1440 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Not Blind 50 NO 1440 Offspring Viability B NO Direct Unstressed -0.088 0.001 1 0.9000000 0.3415260 1440 0.8700000 0.3415260 1440 4.659
50 22 Nandy, B., P. Chakraborty, V. Gupta, S. Z. Ali and N. G. Prasad 2013 Nandy 2013 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Not Blind 50 NO 1440 Offspring Viability B NO Direct Unstressed 0.000 0.001 1 0.9000000 0.3415260 1440 0.9000000 0.3415260 1440 4.659
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 27 Body Size F YES Ambiguous Unstressed -0.089 0.072 1 0.2826667 0.0124900 27 0.2822222 0.0101274 27 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 27 Body Size F YES Ambiguous Unstressed -0.981 0.081 1 0.2943519 0.0103532 27 0.2826667 0.0124900 27 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 27 Body Size F YES Ambiguous Unstressed -1.183 0.085 -1 0.2943519 0.0103532 27 0.2822222 0.0101274 27 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 30 Mating Frequency F YES Indirect Unstressed -0.090 0.065 1 6.7439524 3.1492291 30 7.0200794 2.9816484 30 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 30 Mating Frequency F YES Indirect Unstressed 0.284 0.066 1 7.6940238 3.4353720 30 6.7439524 3.1492291 30 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 30 Mating Frequency F YES Indirect Unstressed 0.205 0.065 1 7.6940238 3.4353720 30 7.0200794 2.9816484 30 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 29 Reproductive Success F NO Direct Unstressed 0.185 0.067 1 50.3663793 7.6774347 29 51.5985906 5.1763110 29 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 29 Reproductive Success F NO Direct Unstressed -0.890 0.075 -1 56.1414116 4.7002918 28 50.3663793 7.6774347 29 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 29 Reproductive Success F NO Direct Unstressed -0.905 0.075 -1 56.1414116 4.7002918 28 51.5985906 5.1763110 29 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 28 Reproductive Success F NO Direct Stressed 0.771 0.076 1 47.9508929 7.0792749 28 52.6177249 4.5419731 27 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 28 Reproductive Success F NO Direct Stressed 0.168 0.067 1 42.6208333 8.3991535 30 47.9508929 7.0792749 28 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 28 Reproductive Success F NO Direct Stressed 1.439 0.087 1 42.6208333 8.3991535 30 52.6177249 4.5419731 27 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 29 Lifespan F NO Indirect Unstressed 1.315 0.081 1 33.4558333 4.1586802 30 38.8756979 3.9717452 29 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 27 Lifespan F NO Indirect Unstressed 0.189 0.074 1 33.2781463 4.5549330 28 33.4558333 4.1586802 30 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 29 Lifespan F NO Indirect Unstressed 0.041 0.067 -1 33.2781463 4.5549330 28 38.8756979 3.9717452 29 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 1.00 32.00 1 1 Blind 45 NO 27 Lifespan F NO Indirect Unstressed -0.669 0.078 1 56.2509143 5.8375189 25 57.2156463 4.2069037 28 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 29 Lifespan F NO Indirect Unstressed 1.295 0.083 1 60.1348639 5.1204446 28 56.2509143 5.8375189 25 4.612
51 22 Nandy, B., V. Gupta, N. Udaykumar, M. A. Samant, S. Sen and N. G. Prasad 2014 Nandy 2014 Drosophila melanogaster Fly 1.000 3.00 32.00 1 1 Blind 45 NO 27 Lifespan F NO Indirect Unstressed -0.612 0.073 1 60.1348639 5.1204446 28 57.2156463 4.2069037 28 4.612
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 3 YES 20 Body Size M YES Ambiguous Unstressed -0.831 0.201 1 NA NA NA NA NA NA 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 3 YES 20 Body Size F YES Ambiguous Unstressed -0.831 0.201 1 NA NA NA NA NA NA 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 3 YES 20 Male Attractiveness M NO Indirect Unstressed 1.999 0.283 1 0.3850000 0.1090000 10 0.6210000 0.1170000 10 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 2 YES 100 Reproductive Success M NO Direct Unstressed 0.415 0.040 1 NA NA NA NA NA NA 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 2 YES 200 Reproductive Success F NO Direct Unstressed -0.118 0.020 1 NA NA NA NA NA NA 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 2 YES 12 Reproductive Success M NO Direct Stressed 0.835 0.313 1 4.5300000 3.5000000 6 9.5400000 7.0100000 6 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 2 YES 12 Reproductive Success M NO Direct Unstressed 0.849 0.314 1 13.5000000 11.5700000 6 23.1800000 9.3500000 6 3.407
52 27 Nelson, A. C., K. E. Colson, S. Harmon and W. K. Potts 2013 Nelson 2013 Mus musculus Mouse 15.000 0.50 30.00 1 1 Blind 2 YES 100 Offspring Viability M NO Direct Unstressed -0.304 0.041 1 NA NA NA NA NA NA 3.407
53 23 Partridge, L. 1980 Partridge 1980 Drosophila melanogaster Fly 100.000 1.00 200.00 1 1 Not Blind 1 YES 41 Offspring Viability B NO Direct Unstressed 0.773 0.103 1 48.9000000 2.9495762 18 51.1000000 2.6645825 23 NA
53 23 Partridge, L. 1980 Partridge 1980 Drosophila melanogaster Fly 100.000 1.00 200.00 1 1 Not Blind 1 YES 35 Offspring Viability B NO Direct Unstressed 0.874 0.125 1 48.1000000 2.4083189 14 49.8000000 1.4832397 21 NA
53 23 Partridge, L. 1980 Partridge 1980 Drosophila melanogaster Fly 100.000 1.00 200.00 1 1 Not Blind 1 YES 60 Offspring Viability B NO Direct Unstressed 0.707 0.069 1 49.4400000 1.4142136 32 50.4500000 1.4142136 28 NA
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 171 Body Size F YES Ambiguous Unstressed 0.080 0.023 1 25.0000000 4.3826932 80 25.3600000 4.5789082 91 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 284 Body Size M YES Ambiguous Unstressed 0.019 0.014 1 16.1800000 1.6099182 127 16.2100000 1.5982097 157 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 284 Male Attractiveness M NO Indirect Unstressed 0.120 0.014 1 1.5900000 0.8624562 127 1.7000000 0.9589258 157 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 284 Male Attractiveness M NO Indirect Unstressed 0.000 0.014 1 3.1300000 0.1437427 127 3.1300000 0.1278568 157 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 284 Male Attractiveness M NO Indirect Unstressed 0.193 0.014 1 0.1600000 0.8624562 127 0.3300000 0.8949974 157 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 284 Male Attractiveness M NO Indirect Unstressed 0.055 0.014 1 150.8900000 7.9058485 127 151.3400000 8.2900267 157 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 174 Reproductive Success F NO Direct Unstressed -0.277 0.023 1 1.5900000 0.7244860 80 1.3820000 0.7659334 94 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 173 Offspring Viability F YES Direct Unstressed 0.621 0.024 1 6.9400000 0.5992662 80 7.3200000 0.6171936 93 3.232
54 31 Pelabon, C., L. K. Larsen, G. H. Bolstad, A. Viken, I. A. Fleming and G. Rosenqvist 2014 Pelabon 2014 Poecilia reticulata Guppy 10.000 1.00 20.00 1 1 Not Blind 9 YES 145 Offspring Viability F NO Direct Unstressed 0.010 0.027 1 3.3200000 2.8195212 73 3.3500000 2.9698485 72 3.232
55 24 Pitnick, S., W. D. Brown and G. T. Miller 2001 Pitnick 2001a Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Not Blind 84 YES 228 Body Size F YES Ambiguous Unstressed 0.973 0.020 1 0.8790000 0.0427083 114 0.9210000 0.0427083 114 NA
55 24 Pitnick, S., W. D. Brown and G. T. Miller 2001 Pitnick 2001a Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Not Blind 84 YES 234 Body Size F YES Ambiguous Unstressed 0.763 0.018 1 0.8950000 0.0432666 117 0.9240000 0.0324500 117 NA
55 24 Pitnick, S., W. D. Brown and G. T. Miller 2001 Pitnick 2001a Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Not Blind 84 YES 230 Reproductive Success F NO Direct Unstressed -0.363 0.018 1 129.1000000 80.4285397 115 99.0000000 84.7180618 115 NA
55 24 Pitnick, S., W. D. Brown and G. T. Miller 2001 Pitnick 2001a Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Not Blind 84 YES 236 Reproductive Success F NO Direct Unstressed -0.246 0.017 1 122.0000000 86.9022439 118 101.2000000 81.4708537 118 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 100 Body Size M YES Ambiguous Unstressed 2.115 0.062 1 233.1300000 16.9400000 50 270.8300000 18.4100000 50 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 100 Body Size M YES Ambiguous Unstressed 1.346 0.048 1 211.6700000 19.8900000 50 237.1900000 17.6800000 50 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 100 Ejaculate Quality and Production M NO Indirect Unstressed 2.886 0.081 1 8.7307692 1.5410000 50 13.7564103 1.9037490 50 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 100 Ejaculate Quality and Production M NO Indirect Unstressed 0.596 0.041 1 7.7820513 2.2663679 50 9.0897436 2.0850585 50 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 30 Ejaculate Quality and Production M NO Indirect Unstressed 1.069 0.145 1 25.5723951 4.4651987 15 30.4600812 4.4023085 15 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 61 YES 30 Ejaculate Quality and Production M NO Indirect Unstressed 1.484 0.163 1 27.4722598 3.3331765 15 32.9769959 3.8991876 15 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 81 YES 30 Ejaculate Quality and Production M NO Indirect Unstressed 0.175 0.127 1 177.4228571 4.2492160 15 178.1600000 3.9836400 15 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 81 YES 30 Ejaculate Quality and Production M NO Indirect Unstressed -1.448 0.161 1 179.7885714 3.1869120 15 174.8857143 3.3860940 15 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 81 YES 178 Mating Success M NO Indirect Unstressed 0.015 0.022 1 NA NA NA NA NA NA NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 81 YES 180 Mating Success M NO Indirect Unstressed 0.148 0.022 1 NA NA NA NA NA NA NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 66 YES 140 Reproductive Success M NO Direct Unstressed -0.436 0.029 1 NA NA NA NA NA NA NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 38 YES 315 Reproductive Success M NO Direct Unstressed 0.022 0.014 1 0.5878581 0.3673826 112 0.5976808 0.4837226 203 NA
56 24 Pitnick, S., G. T. Miller, J. Reagan and B. Holland 2001 Pitnick 2001b Drosophila melanogaster Fly 2.000 3.00 4.00 1 1 Blind 38 YES 344 Reproductive Success M NO Direct Unstressed 0.327 0.012 1 0.4503411 0.4170165 162 0.5968622 0.4754863 182 NA
57 32 Plesnar, A., M. Konior and J. Radwan 2011 Plesnar 2011 Rhizoglyphus robini Mite 1.000 1.00 10.00 1 1 Not Blind 2 NO 80 Offspring Viability M NO Direct Stressed 0.060 0.049 1 0.7700000 0.1700000 40 0.7800000 0.1600000 40 1.029
57 32 Plesnar, A., M. Konior and J. Radwan 2011 Plesnar 2011 Rhizoglyphus robini Mite 1.000 1.00 10.00 1 1 Not Blind 2 NO 80 Offspring Viability M NO Direct Unstressed -0.094 0.049 1 0.9500000 0.1100000 40 0.9400000 0.1000000 40 1.029
58 32 Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Plesnar-Bielak 2012 Rhizoglyphus robini Mite 20.000 1.00 40.00 1 1 Not Blind 14 YES 60 Reproductive Success F NO Direct Stressed 1.504 0.127 1 31.0909091 15.1950949 11 92.8571429 43.4161068 49 5.683
58 32 Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Plesnar-Bielak 2012 Rhizoglyphus robini Mite 20.000 1.00 40.00 1 1 Not Blind 14 YES 95 Reproductive Success F NO Direct Stressed 1.171 0.071 1 134.5000000 48.3000374 48 143.1428571 49.8409939 56 5.683
58 32 Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Plesnar-Bielak 2012 Rhizoglyphus robini Mite 20.000 1.00 40.00 1 1 Not Blind 14 YES 104 Reproductive Success F NO Direct Stressed 0.174 0.038 1 NA NA NA NA NA NA 5.683
58 32 Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Plesnar-Bielak 2012 Rhizoglyphus robini Mite 20.000 1.00 40.00 1 1 Not Blind 14 YES 117 Reproductive Success F NO Direct Stressed 0.526 0.120 1 NA NA NA NA NA NA 5.683
58 32 Plesnar-Bielak, A., A. M. Skrzynecka, Z. M. Prokop and J. Radwan 2012 Plesnar-Bielak 2012 Rhizoglyphus robini Mite 20.000 1.00 40.00 1 1 Not Blind 14 YES 11 Extinction Rate B NO Direct Stressed 1.510 0.740 1 NA NA NA NA NA NA 5.683
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 32 Reproductive Success F NO Direct Stressed 1.331 0.148 1 741.0000000 154.4321210 18 948.0000000 147.7954668 14 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 32 Reproductive Success F NO Direct Stressed 1.339 0.149 1 37.0000000 7.6367532 18 47.4000000 7.4833148 14 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 36 Reproductive Success F NO Direct Unstressed 1.242 0.128 1 602.0000000 143.8255193 18 752.0000000 84.8528137 18 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 36 Reproductive Success F NO Direct Unstressed 1.240 0.128 1 30.1000000 7.2124892 18 37.6000000 4.2426407 18 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 32 Offspring Viability F NO Direct Stressed 1.465 0.154 1 765.0000000 156.9777054 18 978.0000000 118.9847049 14 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 32 Offspring Viability F NO Direct Stressed 1.428 0.153 1 38.3000000 8.0610173 18 48.9000000 5.9866518 14 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 32 Offspring Viability B NO Direct Stressed 1.017 0.137 1 70.4000000 9.7580736 18 79.1000000 5.9866518 14 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 36 Offspring Viability F NO Direct Unstressed 1.194 0.126 1 674.0000000 181.5850214 18 852.0000000 97.5807358 18 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 36 Offspring Viability F NO Direct Unstressed 1.223 0.127 1 33.7000000 8.9095454 18 42.6000000 4.6669048 18 2.747
59 13 Power, D. J. and L. Holman 2014 Power 2014 Callosobruchus maculatus Beetle 1.500 2.00 3.00 0 1 Not Blind 5 YES 36 Offspring Viability B NO Direct Unstressed 1.050 0.122 1 73.0000000 7.6367532 18 79.8000000 4.6669048 18 2.747
60 13 Power, D. J. and L. Holman 2015 Power 2014 Callosobruchus maculatus Beetle 2.000 3.00 4.00 1 0 Blind 3 YES 39 Reproductive Success F NO Direct Unstressed 0.160 0.099 1 0.6091667 0.1700941 20 0.5425014 0.1557092 19 2.747
60 13 Power, D. J. and L. Holman 2015 Power 2014 Callosobruchus maculatus Beetle 2.000 3.00 4.00 1 0 Blind 3 YES 39 Offspring Viability F NO Direct Unstressed -0.396 0.100 1 39.4500000 15.0559483 20 41.7368421 12.7446813 19 2.747
61 25 Promislow, D. E. L., E. A. Smith and L. Pearse 1998 Promislow 1998 Drosophila melanogaster Fly 3.000 5.00 6.00 1 1 Not Blind 13 YES 150 Body Size M YES Ambiguous Unstressed 0.100 0.026 1 -0.0125000 0.2600000 75 0.0168000 0.3190000 75 9.821
61 25 Promislow, D. E. L., E. A. Smith and L. Pearse 1998 Promislow 1998 Drosophila melanogaster Fly 3.000 5.00 6.00 1 1 Not Blind 13 YES 150 Body Size F YES Ambiguous Unstressed -0.449 0.027 1 0.0950000 0.1750000 75 -0.0870000 0.5430000 75 9.821
61 25 Promislow, D. E. L., E. A. Smith and L. Pearse 1998 Promislow 1998 Drosophila melanogaster Fly 3.000 5.00 6.00 1 1 Not Blind 17 YES 10182 Offspring Viability B NO Direct Unstressed 0.006 0.001 1 NA NA NA NA NA NA 9.821
62 32 Radwan, J. 2004 Radwan 2004a Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 2 YES 50 Offspring Viability B NO Direct Stressed 0.739 0.118 1 42.1100000 32.8700000 39 65.3900000 22.6900000 11 3.914
63 32 Radwan, J., J. Unrug, K. Sigorska and K. Gawronska 2004 Radwan 2004b Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 11 YES 92 Reproductive Success F NO Direct Unstressed -0.142 0.043 1 112.7000000 25.1624442 46 108.7000000 30.3170150 46 2.893
63 32 Radwan, J., J. Unrug, K. Sigorska and K. Gawronska 2004 Radwan 2004b Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 11 YES 66 Reproductive Success M NO Direct Unstressed -0.123 0.059 1 0.6170000 0.7180703 33 0.5430000 0.4423313 33 2.893
63 32 Radwan, J., J. Unrug, K. Sigorska and K. Gawronska 2004 Radwan 2004b Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 11 YES 106 Offspring Viability B NO Direct Unstressed 0.106 0.037 1 0.7030000 0.1965630 53 0.7610000 0.7425712 53 2.893
63 32 Radwan, J., J. Unrug, K. Sigorska and K. Gawronska 2004 Radwan 2004b Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 11 YES 90 Lifespan F NO Indirect Unstressed -0.085 0.044 1 25.3700000 21.1979244 45 23.7400000 16.4350996 45 2.893
64 34 Rundle, H. D., S. F. Chenoweth and M. W. Blows 2006 Rundle 2006 Drosophila serrata Fly 55.000 1.00 110.00 1 1 Not Blind 16 YES 552 Reproductive Success B NO Direct Stressed -0.067 0.007 1 30.4100000 40.5200000 276 27.6800000 40.5200000 276 4.292
64 34 Rundle, H. D., S. F. Chenoweth and M. W. Blows 2006 Rundle 2006 Drosophila serrata Fly 55.000 1.00 110.00 1 1 Not Blind 16 YES 552 Reproductive Success B NO Direct Unstressed -0.028 0.007 1 19.5700000 23.5600000 276 18.8300000 28.2700000 276 4.292
66 37 Simmons, L. W. and F. Garcia-Gonzalez 2008 Simmons 2008 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 20 YES 88 Ejaculate Quality and Production M NO Indirect Unstressed 0.918 0.049 1 2.1300000 0.5969925 44 2.6000000 0.3979950 44 4.737
66 37 Simmons, L. W. and F. Garcia-Gonzalez 2008 Simmons 2008 Onthophagus taurus Beetle 10.000 1.00 20.00 1 1 Not Blind 20 YES 88 Body Condition M NO Indirect Unstressed -0.727 0.048 1 NA NA NA NA NA NA 4.737
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Early Fecundity F YES Ambiguous Unstressed 0.205 0.033 1 86.7000000 40.2790268 60 95.5000000 44.9266068 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Early Fecundity F YES Ambiguous Unstressed 0.259 0.033 1 90.7000000 52.6725735 60 102.4000000 35.6314468 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Mating Success M NO Indirect Unstressed 1.768 0.046 1 0.4310000 0.1006976 60 0.6170000 0.1084435 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Mating Success M NO Indirect Unstressed 0.282 0.033 1 0.4760000 0.5654556 60 0.6340000 0.5499636 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Reproductive Success F NO Direct Unstressed 0.022 0.033 1 284.3000000 105.3451470 60 286.8000000 120.8370804 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 120 Reproductive Success F NO Direct Unstressed 0.123 0.033 1 278.0000000 61.1931369 60 284.4000000 39.5044301 60 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 42 Offspring Viability F NO Direct Unstressed -0.287 0.093 1 97.8000000 0.4582576 21 97.5000000 1.3747727 21 4.292
67 32 Tilszer, M., K. Antoszczyk, N. Sa_ek, E. Zaj__c and J. Radwan 2006 Tilszer 2006 Rhizoglyphus robini Mite 5.000 1.00 10.00 1 1 Not Blind 37 YES 42 Offspring Viability F NO Direct Unstressed -0.199 0.092 1 97.4000000 0.9165151 21 96.6000000 5.4990908 21 4.292
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 99 Behavioural Plasticity F YES Ambiguous Unstressed -0.018 0.040 1 285.4200000 196.1144075 50 282.2448980 155.8838417 49 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 98 Behavioural Plasticity F YES Ambiguous Unstressed -0.132 0.040 1 393.4166667 153.0849901 48 371.1800000 179.6165633 50 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 99 Body Size M YES Ambiguous Unstressed 0.155 0.040 1 3.4253300 0.5533225 50 3.5132898 0.4702925 49 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 98 Body Size F YES Ambiguous Unstressed 0.259 0.041 1 4.4623021 0.6760828 48 4.6295060 0.6208700 50 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 99 Ejaculate Quality and Production M NO Indirect Unstressed 0.116 0.040 1 0.2007420 0.0585906 50 0.2075663 0.0648678 49 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 98 Ejaculate Quality and Production M NO Indirect Unstressed -0.022 0.040 1 0.1668542 0.0523804 48 0.1663250 0.0433648 50 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 99 Mating Latency M YES Indirect Unstressed 0.084 0.040 -1 49.2000000 73.2039365 50 44.1836735 40.4874844 49 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 98 Mating Latency F YES Indirect Unstressed -0.105 0.040 1 69.6458333 86.1992964 48 61.4200000 68.2764758 50 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 96 Immunity M NO Ambiguous Unstressed -0.373 0.042 1 12.7920000 0.3350000 49 12.6780000 0.2580000 47 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 94 Immunity F NO Ambiguous Unstressed -0.564 0.044 1 12.9760000 0.2400000 47 12.8530000 0.1880000 47 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 99 Mating Duration M YES Ambiguous Unstressed 0.029 0.040 1 565.1000000 277.6167708 50 572.7551020 244.4586307 49 4.612
68 12 van Lieshout, E., K. B. McNamara and L. W. Simmons 2014 van Lieshout 2014 Callosobruchus maculatus Beetle 1.000 2.00 120.00 1 1 Not Blind 11 NO 98 Mating Duration F YES Ambiguous Unstressed 0.354 0.041 -1 616.3958333 261.4579206 48 530.8400000 217.4206268 50 4.612
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 31 NO 180 Mating Frequency F YES Indirect Unstressed -0.236 0.011 -1 0.3000000 0.5366563 180 0.6700000 2.1466253 180 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 900 Mating Frequency M YES Indirect Unstressed 0.161 0.002 1 0.0390000 0.0900000 900 0.0650000 0.2100000 900 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 31 NO 180 Mating Frequency F YES Indirect Unstressed -0.178 0.011 1 0.2300000 0.1341641 180 0.3000000 0.5366563 180 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 33 NO 900 Mating Frequency M YES Indirect Unstressed -0.077 0.002 1 0.0530000 0.2400000 900 0.2300000 0.1341641 180 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 31 NO 180 Mating Frequency F YES Indirect Unstressed -0.288 0.011 1 0.2300000 0.1341641 180 0.6700000 2.1466253 180 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 900 Mating Frequency M YES Indirect Unstressed 0.053 0.002 1 0.0530000 0.2400000 900 0.0650000 0.2100000 900 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.009 0.126 1 91.0000000 68.9050989 15 91.5000000 35.8880723 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.235 0.127 1 83.0000000 54.5498700 15 68.0000000 68.9050989 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.259 0.127 1 98.0000000 54.5498700 15 81.0000000 71.7761447 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.094 0.126 1 90.5000000 49.5255398 15 86.0000000 43.0656868 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.119 0.126 1 76.0000000 85.4136122 15 84.5000000 48.8077784 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.397 0.129 1 96.5000000 68.9050989 15 73.5000000 40.1946410 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.057 0.126 1 88.0000000 22.9683663 15 91.0000000 68.9050989 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.201 0.127 1 92.0000000 28.7104579 15 83.0000000 54.5498700 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.132 0.127 1 88.0000000 89.0024194 15 98.0000000 54.5498700 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.006 0.126 1 91.0000000 114.8418315 15 90.5000000 49.5255398 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.171 0.127 1 89.0000000 60.2919615 15 76.0000000 85.4136122 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.118 0.126 1 88.5000000 63.1630073 15 96.5000000 68.9050989 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed 0.113 0.126 1 88.0000000 22.9683663 15 91.5000000 35.8880723 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.442 0.129 1 92.0000000 28.7104579 15 68.0000000 68.9050989 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.084 0.126 -1 88.0000000 89.0024194 15 81.0000000 71.7761447 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.056 0.126 1 91.0000000 114.8418315 15 86.0000000 43.0656868 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.080 0.126 1 89.0000000 60.2919615 15 84.5000000 48.8077784 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Reproductive Success F NO Direct Unstressed -0.276 0.127 1 88.5000000 63.1630073 15 73.5000000 40.1946410 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.174 0.007 1 25.5400000 9.6994845 300 27.2600000 10.0458947 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.091 0.007 1 24.0500000 10.3923049 300 25.0300000 11.0851252 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.251 0.007 1 24.9300000 10.3923049 300 27.5900000 10.7387150 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed -0.390 0.007 1 42.3600000 16.8008928 300 36.3500000 13.8564065 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed 0.485 0.007 1 33.3700000 18.5329436 300 43.3200000 22.3434554 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed 0.227 0.007 1 38.4700000 23.2094808 300 43.8100000 23.7290961 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed 0.164 0.127 1 0.8200000 0.4880778 15 0.9000000 0.4593673 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.548 0.131 1 0.9100000 0.2583941 15 0.7200000 0.4019464 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.298 0.128 1 0.8800000 0.2583941 15 0.7600000 0.4880778 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.007 0.007 1 22.2400000 10.0458947 300 22.3100000 10.2190998 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed -0.251 0.007 1 23.9000000 11.9511506 300 21.1700000 9.6994845 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed -0.070 0.007 1 24.2800000 10.7387150 300 23.5500000 10.2190998 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.124 0.007 1 24.2700000 10.2190998 300 25.5400000 9.6994845 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.140 0.007 1 22.7300000 8.8334591 300 24.0500000 10.3923049 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.095 0.007 1 24.0100000 9.1798693 300 24.9300000 10.3923049 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed 0.210 0.007 1 38.2400000 22.3434554 300 42.3600000 16.8008928 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed -0.457 0.007 -1 41.2900000 16.1080725 300 33.3700000 18.5329436 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed -0.202 0.007 1 42.9300000 20.6114046 300 38.4700000 23.2094808 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.069 0.126 1 0.8600000 0.6316301 15 0.8200000 0.4880778 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed 0.040 0.126 1 0.9000000 0.2296837 15 0.9100000 0.2583941 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.058 0.126 1 0.9200000 0.9187347 15 0.8800000 0.2583941 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.061 0.007 1 21.6300000 10.0458947 300 22.2400000 10.0458947 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.159 0.007 1 22.1700000 9.6994845 300 23.9000000 11.9511506 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 1.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.141 0.007 1 22.7800000 10.5655099 300 24.2800000 10.7387150 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.292 0.007 1 24.2700000 10.2190998 300 27.2600000 10.0458947 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.232 0.007 1 22.7300000 8.8334591 300 25.0300000 11.0851252 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 300 Lifespan F NO Indirect Unstressed 0.359 0.007 1 24.0100000 9.1798693 300 27.5900000 10.7387150 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed -0.100 0.007 1 38.2400000 22.3434554 300 36.3500000 13.8564065 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed 0.104 0.007 1 41.2900000 16.1080725 300 43.3200000 22.3434554 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 26 NO 300 Lifespan F NO Indirect Unstressed 0.041 0.007 1 42.9300000 20.6114046 300 43.8100000 23.7290961 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed 0.071 0.126 1 0.8600000 0.6316301 15 0.9000000 0.4593673 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.537 0.131 1 0.9000000 0.2296837 15 0.7200000 0.4019464 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 22 NO 15 Offspring Viability F NO Direct Unstressed -0.211 0.127 1 0.9200000 0.9187347 15 0.7600000 0.4880778 15 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.067 0.007 1 21.6300000 10.0458947 300 22.3100000 10.2190998 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed -0.103 0.007 1 22.1700000 9.6994845 300 21.1700000 9.6994845 300 3.719
69 26 Wigby, S. and T. Chapman 2004 Wigby 2004 Drosophila melanogaster Fly 1.000 3.00 100.00 1 1 Not Blind 33 NO 300 Offspring Viability M NO Direct Unstressed 0.074 0.007 1 22.7800000 10.5655099 300 23.5500000 10.2190998 300 3.719


Study ID: An ID given to the published paper the effect size is sourced from (n=65).

Group ID: An ID given to the research group that may have published several papers on the same species usuing the same or very similar experimental setup. [Was not use in analysis]

Species: The species used in the experimental evolution procedure (n = 15).

Taxon: The taxon to which the species belongs. One of the following: Beetle, fly, mouse, nematode, guppy, mite and cricket (taxa were selected arbitrarily based on the available data).

SS Strength, Ratios and SS Density’s (Column 7-9): Various ratios of the number of males to females and the total number of individuals kept together in an experiment [Was not used in any analysis]

Post cop and Pre cop: Whether a study allowed Pre/Post-copulatory sexual selection (1) or not (0).

Blinding: A binary classification, describing whether blind protocols were used during the experiment. Papers were assumed to be not blind unless declared otherwise.

Generations: The number of generations that the species was subject to differing levels of sexual selection, ranging from 1 to 162.

Enforced Monogamy: Whether the study had the low sexual selection treatment as enforced monogamy (YES) or not (NO). Not all studies compared enforced monogamy and SS+ treatments. Some used FB vs MB, where FB is the SS (low intensity).

n: Pooled sample size of the paired treatments.

Outcome: The fitness related outcome that was measured, e.g. fecundity, survival, or mating success (see Table S1 for all 20 categories). We applied our own classifications rather than relying on those provided by the authors, because different papers sometimes used different names for the same trait.

Outcome Class: To help guide analysis the outcomes were classed into three categories; ambiguous, indirect and direct (see Table S1).

Sex: A moderator variable with three levels, describing whether the effect size in question comes from a measurement of males (M), females (F), or individuals of both sexes (B).

Ambiguous: Is the fitness outcome ambiguous (YES) or not ambigous (NO). Ambiguous outcomes may be those that may not necessarily be directional, that is to say they may be a life history trait.

Environment: In the methods of the papers included in this study it was usually stated whether additional modifications to the experimental lines were made. Briefly, this was usually a modification that made conditions more stressful such as using a novel food source or elevated mutation load, the effect sizes from these experimental lines are labelled as ‘Stressed’. If it was clearly stated that there was no such modification it is labelled ‘Unstressed’. However, sometimes the paper was ambiguous in what lines had added stress or the results from stressed and unstressed lines were pooled together, in this case we label it as ‘Not Stated’.

g: Hedge’s g calculated using the compute.es package.

var.g: The within study variance associated with the effect size, g.

Positive Fitness: Whether the measurment used in the study is beneficial for fitness (1) or not (0). Note that g has already been multiplied by this column. We inverted all of the effect sizes pertaining to fitness outcomes that are expected to be negatively related to fitness by multiplying the effect size by -1.

mean/sd/n.low/high: The means, standard deviation and sample size for the low or high sexual selection treatments, used to calculate lnCVR (meta-analysis of variance). Rows without these values (NA) had hedges g’ derived from summary statistics (F, z, chi-square etc.).

JIF: Journal Impact factor at year of publication. Several impact factors were unable to be determined/found and are NA.We obtained the journal impact factor for each effect size at the time of publication using InCites Journal Citation Reports.


Tables of Sample Sizes

Here we present the number of effect sizes, publications, blind experiments, effect sizes in stresful conditions, male, female and both measures and different species used.

Table S4: Table of effect sizes included in our meta-analysis. See the text following the data table for an explanation of each column.

n.blind.ones <- (sum(prelim.data$Blind == "Blind"))
prelim.data %>% 
  summarise(
    Effect_sizes_.Totalq = n(), 
    Publications = prelim.data$Study.ID %>% unique() %>% length(),
    Blind_experiments = n.blind.ones,
    Effect_sizes_.Enforced_monogamyq = (sum(prelim.data$Enforced.Monogamy == "YES")),
    Effect_sizes_.Ambiguousq = (sum(prelim.data$Outcome.Class == "Ambiguous")),
    Effect_sizes_.Indirectq = (sum(prelim.data$Outcome.Class == "Indirect")),
    Effect_sizes_.Directq = (sum(prelim.data$Outcome.Class == "Direct")),
    Effect_sizes_.Stressedq = (sum(prelim.data$Environment == "Stressed")),
    Effect_sizes_.Unstressedq = (sum(prelim.data$Environment == "Unstressed")),         
    Effect_sizes_.Maleq = (sum(prelim.data$Sex == "M")),
    Effect_sizes_.Femaleq = (sum(prelim.data$Sex == "F")),
    Effect_sizes_.Both_sexesq = (sum(prelim.data$Sex == "B")),
    Different_species =  prelim.data$Species %>% unique() %>% length(),
    Effect_sizes_.Beetleq = sum(Taxon == "Beetle"),
    Effect_sizes_.Flyq = sum(Taxon == "Fly"),
    Effect_sizes_.Mouseq = sum(Taxon == "Mouse"),
    Effect_sizes_.Nematodeq = sum(Taxon == "Nematode"),
    Effect_sizes_.Miteq = sum(Taxon == "Mite"),
    Effect_sizes_.Cricketq = sum(Taxon == "Cricket"),
    Effect_sizes_.Guppyq = sum(Taxon == "Guppy")) %>% melt() %>%
  mutate(variable = gsub("_", " ", variable),
         variable = gsub("[.]", "(", variable),
         variable = gsub("q", ")", variable)) %>% 
  rename_("n" = "value", " " = "variable") %>% 
  pander(split.cell = 40, split.table = Inf)
n
Effect sizes (Total) 459
Publications 65
Blind experiments 54
Effect sizes (Enforced monogamy) 241
Effect sizes (Ambiguous) 96
Effect sizes (Indirect) 147
Effect sizes (Direct) 216
Effect sizes (Stressed) 94
Effect sizes (Unstressed) 335
Effect sizes (Male) 189
Effect sizes (Female) 219
Effect sizes (Both sexes) 51
Different species 15
Effect sizes (Beetle) 116
Effect sizes (Fly) 254
Effect sizes (Mouse) 40
Effect sizes (Nematode) 9
Effect sizes (Mite) 25
Effect sizes (Cricket) 6
Effect sizes (Guppy) 9



Table S5: Table of fitness outcomes included in our meta-analysis by sex.

Outcome_and_sex <- as.data.frame.matrix(table(prelim.data$Outcome, prelim.data$Sex))
colnames(Outcome_and_sex) <- cbind("Both", "Female", "Male")

Outcome_and_sex %>% pander(split.cell = 40, split.table = Inf)
  Both Female Male
Behavioural Plasticity 0 2 0
Body Condition 0 0 1
Body Size 2 13 11
Development Rate 5 1 1
Early Fecundity 0 14 0
Ejaculate Quality and Production 0 0 23
Extinction Rate 4 0 0
Fitness Senescence 0 3 3
Immunity 5 15 15
Lifespan 0 35 3
Male Attractiveness 0 0 6
Mating Duration 0 1 9
Mating Frequency 0 6 5
Mating Latency 0 1 12
Mating Success 0 0 39
Mutant Frequency 6 0 2
Offspring Viability 15 26 15
Pesticide Resistance 2 0 0
Reproductive Success 12 102 42
Strength 0 0 2

Forest Plot

# Create new factor to order factors in a way where Ambig, Indirect and Direct are Grouped
prelim.data$Outcome_f = factor(prelim.data$Outcome, levels = c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success'))

# define upper and lower bounds
prelim.data$lowerci <- prelim.data$g - 1.96*(sqrt(prelim.data$var.g))
prelim.data$upperci <- prelim.data$g + 1.96*(sqrt(prelim.data$var.g))

library(ggthemes)
#Generate a plot
p.meta <- prelim.data %>% 
  mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
         Sex = replace(Sex, Sex == "M", "Male"),
         Sex = replace(Sex, Sex == "F", "Female"),
         Outcome.Class = factor(Outcome.Class, levels = c("Ambiguous", "Indirect", "Direct"))) %>%

  
  ggplot(aes(y=reorder(AuthorYear, -g), x = g)) +
  
  scale_color_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"), 
                    name = "Relationship\nto fitness")+
  
  scale_shape_manual(values=c(21,22,24))+
  
    
  scale_fill_manual(values = c("Ambiguous" = "#a50f15", "Indirect" = "#fe9929", "Direct" = "#4daf4a"), 
                    name = "Relationship\nto fitness")+
  
  
  geom_errorbarh(aes(xmin = lowerci, 
                     xmax = upperci,
                     color = Outcome.Class), height = 0.1, show.legend = FALSE) +
  

  geom_point(aes(shape = Sex,
                 fill = Outcome.Class), 
             size = 1.75, 
             color = "grey20") +
  
  scale_x_continuous(limits=c(-3.35, 12), 
                     breaks = c(-3, -2, -1, 0, 1, 2, 3), 
                     name='                              Standardized Mean Difference (g) \n[positive values indicate sexual selection improves fitness components]') +

  ylab('Reference') + 
  
  geom_vline(xintercept=0, 
             color='black', 
             linetype='dashed')+
  
  facet_grid(Outcome_f~.,
             labeller = label_wrap_gen(width=23),
             scales= 'free', 
             space='free')+
  
  guides(fill = guide_legend(override.aes = list(shape = 21, colour = "grey20", size = 6)),
         shape = guide_legend(override.aes = list(size = 4.5)))+
  
  #Add theme specifying text size, margins, lines etc.
  theme_bw()+
  
  theme(strip.text.y = element_text(angle = 0, size = 8, margin = margin(t=15, r=15, b=15, l=15)), 
        strip.background = element_rect(colour = NULL,
                                        linetype = "blank",
                                        fill = "gray90"),
        text = element_text(size=11),
        panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_line(linetype = "solid", colour = "gray95"),
        panel.grid.major.y = element_line(linetype = "solid", color = "gray95"),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(), 
        legend.text = element_text(size=12), 
        legend.title=element_text(size=12, 
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0, size = 12))


### We can save the file in several vector graphics forms

#ggsave(plot = p.meta, filename = "figures/Big_forest_plot.eps", height = 18, width = 12)

# svg("figures/Big_forest_plot.svg", width=12, height=18)
# p.meta
# dev.off()
# 
# pdf("figures/Big_forest_plot.pdf", width=12, height=18)
# p.meta
# dev.off()

p.meta


Figure S1: Forest plot of raw effect sizes and their 95% confidence intervals, grouped according to measured fitness components and the sex of the individuals whose fitness trait was measured (male, female, or both sexes mixed together). Rows with multiple data points denote studies that provided multiple effect sizes. Positive values indicate fitness benefits of sexual selection.


Meta-Analysis

Overall effect of sexual selection on fitness

We can obtain an overall weghted grand mean and confidence intervals with a simple intercept only for both Bayesian and REML models. Notably, in both models the estimates are approximately the same, with Bayesian estimates being marginally wider.

grand.mean.bayes <- brm(g | se(SE)  ~ 1 #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
                + (1|Study.ID)
                + (1|Outcome), 
                family = "gaussian", 
                seed = 1,
                cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
                control = list(adapt_delta = 0.9999, max_treedepth = 15),
                data = prelim.data %>% mutate(SE = sqrt(var.g)))
grand.mean.bayes <- readRDS("data/grand.mean.bayes.rds")

#Run REML model 
forest.model <- rma.mv(g, var.g,
                       mods = ~ 1,
                       random = list(~ 1 | Study.ID,
                                      ~ 1 | Outcome),
                       method = "REML",
                       data = prelim.data)

#Manually imput bayesian results
mean = c(forest.model$b, 0.22)
LCI = c(forest.model$ci.lb, 0.01)
UCI = c(forest.model$ci.ub, 0.43)
grand.means.df <- data.frame(rbind(mean, LCI, UCI))
colnames(grand.means.df) = c("REML", "Bayesian")
grand.means.df %>% pander(digits = 2)
  REML Bayesian
mean 0.22 0.22
LCI 0.03 0.01
UCI 0.41 0.43

Effect of Sexual Selection for different fitness components

Instead of running individual models for each fitness component we can run a model with the fitness components as predictors. In this case we maintain all of our fitness components and include study.id as a group level effect (to account for within study correlations in effect size). Using the brms package we can run a Bayesian model and generate fitted values for each fitness component.

model.fitness.components.bayes <- brm(g | se(SE)  ~ Outcome #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
                + (1|Study.ID), 
                family = "gaussian", 
                seed = 1,
                cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
                control = list(adapt_delta = 0.999, max_treedepth = 15),
                data = prelim.data %>% mutate(SE = sqrt(var.g)))

make_text_summary(model.fitness.components.bayes) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander(split.cell = 40, split.table = Inf)

Table S6 Summary of model predictions for 20 fitness components. In the manuscript these values are presented as a text overlay on Figure 1 (Forest Plot).

components.brms <- readRDS(file = "data/components.brms.rds") #Avoid re-running model above

#Expand grid for environment and sex
brms.newdata <- as.data.frame(expand.grid(Outcome = unique(prelim.data$Outcome)))

#Get average SE: useful if using predict, but not fitted
av.se.g <- prelim.data %>% group_by(Outcome) %>% summarise(mean = mean(sqrt(var.g)))
brms.newdata$SE <- av.se.g$mean

#Add predictions/fitted values
brms.predict <- fitted(components.brms, newdata = brms.newdata, re_formula = NA)
brms.predict <- as.data.frame(brms.predict)
brms.predictions <- data.frame(brms.newdata$Outcome, brms.predict$Estimate, brms.predict$Est.Error, brms.predict$Q2.5, brms.predict$Q97.5)

#Name columns
colnames(brms.predictions) <- c("Fitness Component", "Estimate", "Error", "LCI", "UCI")

outcome.list.factor <-  c('Behavioural Plasticity', 'Body Size', 'Development Rate', 'Early Fecundity', 'Immunity', 'Mating Duration', 'Pesticide Resistance', 'Mutant Frequency', 'Body Condition', 'Fitness Senescence', 'Lifespan', 'Male Attractiveness', 'Mating Frequency', 'Mating Latency', 'Mating Success', 'Strength', 'Ejaculate Quality and Production', 'Extinction Rate', 'Offspring Viability', 'Reproductive Success')

brms.predictions <- brms.predictions[match(outcome.list.factor, brms.predictions$`Fitness Component`),]
rownames(brms.predictions) <- NULL
sample.sizes.outcomes <- as.data.frame(table(prelim.data$Outcome))
colnames(sample.sizes.outcomes) <- c("Fitness Component", "n")
fitness.component.predictions <- left_join(brms.predictions, sample.sizes.outcomes, by = "Fitness Component")
fitness.component.predictions %>% add_significance_stars2() %>% pander(split.cell = 40, split.table = Inf, digits = 2)
Fitness Component Estimate Error LCI UCI n
Behavioural Plasticity 0.13 0.17 -0.2 0.46 2
Body Size 0.36 0.071 0.22 0.5 26 *
Development Rate 0.53 0.12 0.28 0.76 7 *
Early Fecundity 0.31 0.13 0.049 0.56 14 *
Immunity -0.49 0.11 -0.71 -0.28 35 *
Mating Duration 0.16 0.082 -0.011 0.32 10
Pesticide Resistance 1.1 0.49 0.16 2.1 2 *
Mutant Frequency 0.27 0.34 -0.4 0.97 8
Body Condition -1.3 0.31 -1.9 -0.68 1 *
Fitness Senescence 0.68 0.076 0.54 0.83 6 *
Lifespan 0.24 0.068 0.1 0.37 38 *
Male Attractiveness 0.3 0.11 0.088 0.51 6 *
Mating Frequency 0.19 0.072 0.041 0.32 11 *
Mating Latency 0.69 0.073 0.54 0.83 13 *
Mating Success -0.082 0.071 -0.22 0.052 39
Strength 0.18 0.14 -0.09 0.46 2
Ejaculate Quality and Production 0.26 0.083 0.091 0.42 23 *
Extinction Rate 0.37 0.18 0.022 0.71 4 *
Offspring Viability 0.19 0.067 0.056 0.32 56 *
Reproductive Success 0.16 0.065 0.029 0.29 156 *

Models With Many Covariates

We collected data from fitness components that were deemed ambiguous as well as unambiguous. The ambiguous outcomes are likely to add in heterogeneity to the models and may not help us in answering questions of the fitness effects of sexual selection. A REML model utilising our complete dataset with many moderator variables would thus be:

model.preliminary <- rma.mv(g, var.g, 
                         mods = ~ 1 + Sex * Environment + Taxon + Outcome.Class + log(Generations) + Blinding, # << ----- Add big model, then cull predictors to this one
                         random = list(~ 1 | Study.ID, 
                                       ~ 1 | Outcome), 
                         method = "REML", 
                         data = prelim.data)

summary(model.preliminary, digits = 2)
## 
## Multivariate Meta-Analysis Model (k = 459; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc  
## -1706.78   3413.56   3455.56   3541.39   3457.77  
## 
## Variance Components: 
## 
##            estim  sqrt  nlvls  fixed    factor
## sigma^2.1   0.23  0.48     65     no  Study.ID
## sigma^2.2   0.14  0.37     20     no   Outcome
## 
## Test for Residual Heterogeneity: 
## QE(df = 440) = 5609.94, p-val < .01
## 
## Test of Moderators (coefficient(s) 2:19): 
## QM(df = 18) = 49.38, p-val < .01
## 
## Model Results:
## 
##                             estimate    se   zval  pval  ci.lb  ci.ub   
## intrcpt                         0.38  0.28   1.36  0.17  -0.17   0.93   
## SexF                            0.05  0.04   1.09  0.28  -0.04   0.14   
## SexM                            0.04  0.04   1.05  0.30  -0.04   0.13   
## EnvironmentNot Stated           0.12  0.13   0.89  0.37  -0.14   0.37   
## EnvironmentStressed             0.05  0.06   0.82  0.41  -0.06   0.16   
## TaxonCricket                    0.17  0.55   0.30  0.76  -0.91   1.25   
## TaxonFly                       -0.26  0.16  -1.60  0.11  -0.57   0.06   
## TaxonGuppy                     -0.35  0.50  -0.70  0.49  -1.34   0.64   
## TaxonMite                      -0.08  0.25  -0.31  0.75  -0.56   0.41   
## TaxonMouse                     -0.36  0.21  -1.74  0.08  -0.76   0.05  .
## TaxonNematode                  -0.27  0.51  -0.52  0.60  -1.26   0.73   
## Outcome.ClassAmbiguous         -0.00  0.20  -0.01  0.99  -0.39   0.39   
## Outcome.ClassDirect             0.01  0.26   0.04  0.97  -0.49   0.51   
## log(Generations)                0.01  0.05   0.12  0.90  -0.10   0.11   
## BlindingNot Blind              -0.07  0.21  -0.33  0.74  -0.49   0.35   
## SexF:EnvironmentNot Stated      0.03  0.10   0.27  0.79  -0.18   0.23   
## SexM:EnvironmentNot Stated      0.00  0.09   0.03  0.98  -0.17   0.18   
## SexF:EnvironmentStressed        0.09  0.06   1.36  0.17  -0.04   0.21   
## SexM:EnvironmentStressed       -0.13  0.06  -2.07  0.04  -0.26  -0.01  *
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Here we can also run a Bayesian model alongside the REML model (metafor). The R2 for this model is 0.36 (95% CIs = 0.33-0.4).

#Also use bayesian model
brms.preliminary <- brm(g | se(SE)  ~ 1 + Sex * Environment + log(Generations) + Blinding #Note that running se(SE, sigma = TRUE) gives different result due to a difference in priors
                + (1|Study.ID) #group level effects
                + (1|Outcome)
                + (1|Taxon), 
                family = "gaussian", 
                seed = 1,
                cores = 4, chains = 4, iter = 4000, #Run 4 chains in parallel for 4000 iterations (2000 are burn in)
                control = list(adapt_delta = 0.999, max_treedepth = 15),
                data = prelim.data %>% mutate(SE = sqrt(var.g)))


brms.preliminary <- readRDS(file = "data/brms.preliminary.rds") #Avoid re-running model above
#Plot model results
prelim.results.bayesplot <- bayesplot::mcmc_areas(posterior_samples(brms.preliminary)[,1:11]) + 
  geom_vline(xintercept = 0, linetype = 2) +
  
  theme_bw()+
  
  theme(panel.spacing = unit(0.1, "lines"),
        text = element_text(size=16),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(), 
        legend.text = element_text(size=16), 
        legend.title=element_text(size=16, 
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 14),
        axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
        axis.text.y = element_text(angle = 0),
        plot.title = element_text(size = 16))

prelim.results.bayesplot

make_text_summary(brms.preliminary) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()
Model Parameter Estimate Est.Error Q2.5 Q97.5
b_Intercept 0.271 0.242 -0.214 0.758
b_SexF 0.046 0.044 -0.042 0.134
b_SexM 0.044 0.043 -0.04 0.129
b_EnvironmentNotStated 0.105 0.127 -0.141 0.361
b_EnvironmentStressed 0.047 0.057 -0.063 0.158
b_logGenerations -0.005 0.048 -0.098 0.092
b_BlindingNotBlind -0.099 0.194 -0.475 0.285
b_SexF:EnvironmentNotStated 0.029 0.102 -0.171 0.224
b_SexM:EnvironmentNotStated 0.001 0.089 -0.176 0.178
b_SexF:EnvironmentStressed 0.084 0.062 -0.035 0.205
b_SexM:EnvironmentStressed -0.138 0.064 -0.263 -0.013 *
sd_Outcome__Intercept 0.366 0.084 0.236 0.564 *
sd_Study.ID__Intercept 0.476 0.053 0.385 0.588 *
sd_Taxon__Intercept 0.162 0.141 0.006 0.513 *

Figure S2 & Table S7: Bayesian model results for a preliminary model that explores many covariates collected in the dataset.

From these models we can see that there are several redundant moderators: Blinding and Generations show little effect and are not key to our research question (like sex and environment are). However, because taxa is a likely source of heterogeneity and effect size could reasonably be expected to differ between taxa, we investigate taxa as a fixed effect.


Sexual Selection Amongst Taxa

First we want to run the model using a restricted dataset where we remove effect sizes with Ambiguous outcomes (directionless or variable in their relation to fitness) or environments that were not stated whether they were stressed or unstressed (confusing and confounding). Notably, with the ambiguous measures included we already see a positive effect of sexual selection and thus removing these fitness components is not an attempt to obtain a significant answer, rather it restricts the data to traits that are less contentious and thus we believe this approach is conservative. In this model we use Sex, Environment, Taxon and the interaction between sex and environment.

#Restrict the dataset for unambiguous outcomes and environments 
restricted.data <- prelim.data %>% 
  filter(Outcome.Class != "Ambiguous" & Environment != "Not Stated") %>% 
  mutate(Sex = as.character(Sex), 
         Environment = as.character(Environment), 
         Outcome.Class.2 = as.character(Outcome.Class), 
         Enforced.Monogamy = as.character(Enforced.Monogamy))

# Make sure the factors are leveled in the same order as we write our prediction function (below)
restricted.data$Environment <- restricted.data$Environment %>% factor() %>% relevel(ref="Unstressed")
restricted.data$Sex <- restricted.data$Sex %>% factor() %>% relevel(ref="M")
restricted.data$Outcome.Class <- restricted.data$Outcome.Class %>% factor() %>% relevel(ref="Indirect")
restricted.data$Taxon <- relevel(restricted.data$Taxon, ref = "Beetle") 
restricted.data$Outcome <- restricted.data$Outcome %>% factor()

model.complete <- rma.mv(g, V = var.g, 
                         mods = ~ 1 + Sex * Environment + Taxon, # << ----- Add big model, then cull predictors to this one
                         random = list(~ 1 | Study.ID, 
                                       ~ 1 | Outcome), 
                         method = "REML", 
                         data = restricted.data)

summary(model.complete, digits = 2)
## 
## Multivariate Meta-Analysis Model (k = 336; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc  
## -1424.00   2848.00   2874.00   2923.19   2875.17  
## 
## Variance Components: 
## 
##            estim  sqrt  nlvls  fixed    factor
## sigma^2.1   0.22  0.47     56     no  Study.ID
## sigma^2.2   0.12  0.34     13     no   Outcome
## 
## Test for Residual Heterogeneity: 
## QE(df = 325) = 4443.60, p-val < .01
## 
## Test of Moderators (coefficient(s) 2:11): 
## QM(df = 10) = 58.87, p-val < .01
## 
## Model Results:
## 
##                           estimate    se   zval  pval  ci.lb  ci.ub     
## intrcpt                       0.35  0.17   2.05  0.04   0.02   0.68    *
## SexB                         -0.07  0.06  -1.03  0.30  -0.19   0.06     
## SexF                          0.05  0.03   1.92  0.06  -0.00   0.11    .
## EnvironmentStressed          -0.12  0.04  -2.86  <.01  -0.20  -0.04   **
## TaxonCricket                 -0.02  0.50  -0.05  0.96  -1.01   0.96     
## TaxonFly                     -0.18  0.17  -1.11  0.27  -0.51   0.14     
## TaxonGuppy                   -0.21  0.50  -0.42  0.67  -1.18   0.76     
## TaxonMite                     0.02  0.24   0.07  0.95  -0.46   0.50     
## TaxonMouse                   -0.33  0.24  -1.39  0.16  -0.79   0.14     
## SexB:EnvironmentStressed      0.16  0.08   1.96  0.05  -0.00   0.32    .
## SexF:EnvironmentStressed      0.25  0.05   4.89  <.01   0.15   0.34  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The result is a model with estimates for various taxa, species, sexes and environments. To make sense of these estimates we should obtain average predictions for each moderator variable class of interest. We can do that by using a modified version version of a function used by Holman (2018). Here it alows us to cluster predictions for the different moderators of interest: Sex, environment, taxon etc. This is done by obtaining predictions using the base predict() function for the rma.mv() objects that have been previously created

# function that makes predict.rma work like a normal predict() function, instead of the idiosyncratic way that it works by default.
get.predictions.complete <- function(newdata){
  B<-0; F<-0; Stressed<-0; Cricket<-0; Fly<-0; Guppy<-0; Mite<-0; Mouse<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Stressed<-1
  if(newdata[3] == "Cricket") Cricket<-1
  if(newdata[3] == "Fly") Fly<-1
  if(newdata[3] == "Guppy") Guppy<-1
  if(newdata[3] == "Mite") Mite<-1
  if(newdata[3] == "Mouse") Mouse<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(model.complete, newmods=c(B, F, Stressed, Cricket, Fly, Guppy, Mite, Mouse, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.complete <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed"),
                           Taxon = c("Beetle", "Cricket", "Fly", "Guppy", "Mite", "Mouse")))
predictions.complete <- cbind(predictions.complete, do.call("rbind", apply(predictions.complete, 1, get.predictions.complete))) %>%
  select(Sex, Environment, Taxon, pred, se, ci.lb, ci.ub) 
for(i in 4:7) predictions.complete[,i] <- unlist(predictions.complete[,i])

countpred = count_(restricted.data, c("Sex", "Environment", "Taxon"))

predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))

countpred = count_(restricted.data, c("Sex", "Environment", "Taxon"))

predictions.complete <- left_join(predictions.complete, countpred, by = c("Sex", "Environment", "Taxon"))



#Thirdly, plot the model predictions for effect size (Hedges' g) for male, female and both sexes under both stressed and unstressed condition and faceted for each taxon. 


pd <- position_dodgev(0.6)

Taxon.metaanlysis <- predictions.complete %>% 
    mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
         Sex = replace(Sex, Sex == "M", "Male"),
         Sex = replace(Sex, Sex == "F", "Female"),
         Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
         Environment = replace(Environment, Environment == "Unstressed", "Benign"),
         Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>%
  ggplot(aes(x = pred, y= Environment, fill = Sex)) + 
  geom_vline(xintercept = 0, linetype = 2, colour = "black") + 
  geom_errorbarh(aes(xmin = predictions.complete$ci.lb, 
                     xmax = predictions.complete$ci.ub,
                     color= Sex), 
                 height = 0, position = pd, show.legend = F) +
  geom_point(position = pd, size=2, shape=21, color = "grey20") + 
  facet_grid(Taxon ~.)+
  ylab("Environment \n")+
  xlab("\nModel Prediction (Hedges g)")+
  xlim(-1, 2)+
  ggtitle('Effects of Sex and Stress on \nPopulation Fitness for Each Taxon')+
  scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  guides(fill = guide_legend(reverse=T, override.aes = list(size = 4.5)))+
  
    theme_bw()+
  
  theme(strip.text.y = element_text(angle = 0, size = 14, margin = margin(r=20, l=20)), 
        strip.background = element_rect(colour = NULL,
                                        linetype = "blank",
                                        fill = "gray90"),
        text = element_text(size=14),
        panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(), 
        legend.text = element_text(size=14), 
        legend.title=element_text(size=14, 
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0.3, size = 14),
        axis.title.y = element_text(size = 14),
        plot.title = element_text(size = 14))

#ggsave(plot = Taxon.metaanlysis, filename = "figures/Taxon_metaanalysis_plot.svg", height = 8, width = 8)
Taxon.metaanlysis


Figure S3: The predictions from this model indicate some heterogeneity between taxon. However, the most apparent difference between taxa is that confidence bands increase for taxa with low sample size. As previously shown, the beetle and fly taxa are the most heavily sampled and in the above figure have the narrowest confidence bands. Importantly, the overall direction of effect does not change between taxon, although guppies and mice show near zero effect sizes. Here we see that under stressed environments, females from all taxa appear to have greater fitness increase than males or ‘both’.

Table S8 The predictions for the above figure looking at the effect of sexual selection amongst taxa uses the following dataframe.

colnames(predictions.complete) <- c("Sex", "Environment", "Taxon", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete <- format(predictions.complete, digits = 2)
predictions.complete[[9]] <- NULL
predictions.complete %>% pander()
Sex Environment Taxon Prediction SE CI.lb CI.ub n
M Unstressed Beetle 0.3462 0.17 0.015 0.68 31
B Unstressed Beetle 0.2804 0.18 -0.065 0.63 2
F Unstressed Beetle 0.4006 0.17 0.068 0.73 15
M Stressed Beetle 0.2279 0.17 -0.109 0.57 2
B Stressed Beetle 0.3229 0.18 -0.027 0.67 6
F Stressed Beetle 0.5277 0.17 0.193 0.86 9
M Unstressed Cricket 0.3212 0.49 -0.648 1.29 1
B Unstressed Cricket 0.2554 0.50 -0.719 1.23 NA
F Unstressed Cricket 0.3756 0.50 -0.595 1.35 NA
M Stressed Cricket 0.2030 0.50 -0.769 1.18 NA
B Stressed Cricket 0.2980 0.50 -0.681 1.28 NA
F Stressed Cricket 0.5028 0.50 -0.469 1.47 NA
M Unstressed Fly 0.1618 0.14 -0.105 0.43 73
B Unstressed Fly 0.0960 0.14 -0.186 0.38 9
F Unstressed Fly 0.2162 0.14 -0.053 0.48 93
M Stressed Fly 0.0436 0.14 -0.230 0.32 13
B Stressed Fly 0.1386 0.15 -0.154 0.43 8
F Stressed Fly 0.3434 0.14 0.073 0.61 19
M Unstressed Guppy 0.1372 0.48 -0.813 1.09 4
B Unstressed Guppy 0.0715 0.49 -0.889 1.03 NA
F Unstressed Guppy 0.1916 0.48 -0.758 1.14 3
M Stressed Guppy 0.0190 0.49 -0.933 0.97 NA
B Stressed Guppy 0.1140 0.49 -0.848 1.08 NA
F Stressed Guppy 0.3188 0.49 -0.633 1.27 NA
M Unstressed Mite 0.3626 0.23 -0.082 0.81 4
B Unstressed Mite 0.2968 0.23 -0.160 0.75 2
F Unstressed Mite 0.4170 0.23 -0.028 0.86 7
M Stressed Mite 0.2444 0.23 -0.204 0.69 1
B Stressed Mite 0.3394 0.23 -0.118 0.80 4
F Stressed Mite 0.5442 0.23 0.098 0.99 5
M Unstressed Mouse 0.0165 0.22 -0.411 0.44 6
B Unstressed Mouse -0.0492 0.23 -0.492 0.39 2
F Unstressed Mouse 0.0709 0.22 -0.358 0.50 5
M Stressed Mouse -0.1017 0.22 -0.531 0.33 7
B Stressed Mouse -0.0067 0.23 -0.456 0.44 NA
F Stressed Mouse 0.1981 0.22 -0.232 0.63 5

Given that none of categories of taxa significantly impact effect size, and many categories are unsamples (Table S8) we can incorporate them as random/group level effects in models henceforth.


REML: Effects of Environment and Sex

Here we ask two key questions: Does sexual selection benefit populations in stressful environments more than benign environments? AND Do the benefits of sexual selection differ between the sexes?

#We can run a model where the outcome is crossed with study (Study.ID) and Taxon as random effects and environment, sex and their interactions are fixed effects.
model.complete2 <- rma.mv(g, V = var.g, 
                          mods = ~ 1 + Sex * Environment, 
                          random = list(~ 1 | Study.ID, 
                                       ~ 1 | Outcome,
                                       ~ 1 | Taxon), 
                          method = "REML", 
                          data = restricted.data)
summary(model.complete2, digits = 2)
## 
## Multivariate Meta-Analysis Model (k = 336; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc  
## -1429.57   2859.14   2877.14   2911.33   2877.70  
## 
## Variance Components: 
## 
##            estim  sqrt  nlvls  fixed    factor
## sigma^2.1   0.20  0.45     56     no  Study.ID
## sigma^2.2   0.11  0.34     13     no   Outcome
## sigma^2.3   0.00  0.00      6     no     Taxon
## 
## Test for Residual Heterogeneity: 
## QE(df = 330) = 4576.52, p-val < .01
## 
## Test of Moderators (coefficient(s) 2:6): 
## QM(df = 5) = 55.94, p-val < .01
## 
## Model Results:
## 
##                           estimate    se   zval  pval  ci.lb  ci.ub     
## intrcpt                       0.21  0.12   1.82  0.07  -0.02   0.44    .
## SexB                         -0.06  0.06  -1.02  0.31  -0.19   0.06     
## SexF                          0.05  0.03   1.90  0.06  -0.00   0.11    .
## EnvironmentStressed          -0.12  0.04  -2.94  <.01  -0.20  -0.04   **
## SexB:EnvironmentStressed      0.17  0.08   2.06  0.04   0.01   0.33    *
## SexF:EnvironmentStressed      0.25  0.05   4.95  <.01   0.15   0.35  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

We conducted hypothesis tests on the above model to investigate the effective difference between two groups. Depending on the package this method is also refered to as linear hypothesis or anova (metafor).

Table S9 Using the anova.rma function we can conduct hypothesis tests between two categorical groups in the model. Here we conduct 5 tests testing relative effects of sexual selection between the sexes and in different environments.

#anova where you specify the values based on the list of moderators
anova.1 = anova(model.complete2, L=c(0, 0, -1, 0, 0, 0)) 
anova.2 = anova(model.complete2, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(model.complete2, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(model.complete2, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(model.complete2, L=c(0, 0, 0, -1, -1, 0))

anova.list <- list(anova.1, anova.2, anova.3, anova.4, anova.5)

anova.frame <- t(data.frame(lapply(anova.list, function(x) {
  data.frame(x[["hyp"]],
  x[["Lb"]],
  x[["se"]],
  x[["Lb"]] - 1.96*x[["se"]],
  x[["Lb"]] + 1.96*x[["se"]],
  x[["pval"]])
})))
anova.frame <- as.data.frame(split(anova.frame, rep(1:6)))
colnames(anova.frame) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame$Estimate <- as.numeric(levels(anova.frame$Estimate))[anova.frame$Estimate]
anova.frame$Est.Error <- as.numeric(levels(anova.frame$Est.Error))[anova.frame$Est.Error]
anova.frame$CI.Lower <- as.numeric(levels(anova.frame$CI.Lower))[anova.frame$CI.Lower]
anova.frame$CI.Upper <- as.numeric(levels(anova.frame$CI.Upper))[anova.frame$CI.Upper]
anova.frame$pval <- as.numeric(levels(anova.frame$pval))[anova.frame$pval]
anova.frame <- format(anova.frame, digits = 2)
anova.frame$star <- c("", "*", "*", "*", "")
colnames(anova.frame)[colnames(anova.frame)=="star"] <- " "
anova.frame$pval <- NULL
rownames(anova.frame) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame %>% pander(split.table = Inf)
  Hypothesis Estimate Est.Error CI.Lower CI.Upper
M vs F, Benign -SexF = 0 -0.054 0.028 -0.11 0.0016
M vs F, Stressful -SexF - SexF:EnvironmentStressed = 0 -0.302 0.044 -0.39 -0.2161 *
Benign vs Stressful, Female -EnvironmentStressed - SexF:EnvironmentStressed = 0 -0.127 0.037 -0.20 -0.0545 *
Benign vs Stressful, Male -EnvironmentStressed = 0 0.121 0.041 0.04 0.2020 *
Benign vs Stressful, Both -EnvironmentStressed - SexB:EnvironmentStressed = 0 -0.047 0.074 -0.19 0.0970


From these anovas we see that a stressful environment leads to a significantly greater increase in fitness components for “female”" but NOT “both” sexes, while significantly decreasing male fitness. We also see there is a difference between the sexes in stressful environments (females with larger mean) but not in benign environments (marginal).


Bayesian: Effects of Environment and Sex

We can run a Bayesian model using the brms package. The model has the same moderators as the REML approach used above. The brms package sets standard priors that are selected to be ‘weakly informative’. The R2 of this Bayesian model was 0.35 (95% CIs = 0.31-0.39). Additionally, we can obtain the distribution of posterior fitted values and examine the model summary

#Use brms to create a model similar to the one used in the REML approach. 
brms.complete2 <- brm(g | se(SE)  ~ 1 + Sex * Environment #se(SE, sigma = TRUE) gives differnt results
                + (1|Taxon) #group level effect #1
                + (1|Study.ID) #group level effect #2
                + (1|Outcome), #group level effect #3 
                family = "gaussian", 
                seed = 1,
                cores = 4, chains = 4, iter = 4000, # Run 4 chains in parallel for 4000 iterations (2000 are burn in)
                control = list(adapt_delta = 0.9999, max_treedepth = 15),
                data = restricted.data %>% mutate(SE = sqrt(var.g)))


Table S10 Model estimate summary table for the Bayesian model investigating the effect of environment and sex (alongside sexual selection) on fitness.

brms.complete2 <- readRDS(file = "data/brms.complete2.rds") #Avoid re-running model above

#You can obtain a posterior sampling through the ``fitted.brmsfit`` function, which gives the same posterior values as doing it manually (see below).**Note that the fitted values obtained here have much smaller error than the predict values**


# #obtain average variance of each group in the prediction
# av.var.g <- as.numeric(c(restricted.data %>% filter (Sex == 'M' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
#               restricted.data %>% filter (Sex == 'B' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
#               restricted.data %>% filter (Sex == 'F' & Environment == "Unstressed") %>% summarise(mean = mean(var.g)),
#               restricted.data %>% filter (Sex == 'M' & Environment == "Stressed") %>% summarise(mean = mean(var.g)),
#               restricted.data %>% filter (Sex == 'B' & Environment == "Stressed") %>% summarise(mean = mean(var.g)),
#               restricted.data %>% filter (Sex == 'F' & Environment == "Stressed") %>% summarise(mean = mean(var.g))))

#Expand grid for environment and sex
# brms.newdata <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
#                            Environment = c("Unstressed", "Stressed")))
# #Add variance
# brms.newdata$var.g <- av.var.g
# 
# #Add predictions
# brms.predict <- fitted(meta.brms, newdata = brms.newdata, re_formula = NA)
# brms.predict <- as.data.frame(brms.predict)
# brms.predictions <- data.frame(brms.newdata$Sex, brms.newdata$Environment, brms.predict$Estimate, brms.predict$Est.Error, brms.predict$Q2.5, brms.predict$Q97.5)
# 
# #Name columns
# colnames(brms.predictions) <- c("Sex", "Environment", "Estimate", "Error", "LCI", "UCI")


#Alternatively you can obtain posterior samples manually.
post <- (posterior_samples(brms.complete2, 
                           pars = c("b_Intercept", "b_SexB", "b_SexF", 
                                    "b_EnvironmentStressed", "b_SexB:EnvironmentStressed", 
                                    "b_SexF:EnvironmentStressed")) %>%
         mutate(both_benign = b_Intercept + b_SexB,
         both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
         male_benign = b_Intercept,
         male_stressful = b_Intercept + b_EnvironmentStressed,
         female_benign = b_Intercept + b_SexF,
         female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]


#Obtain posterior fitted values and transform
# posterior_fit <- data.frame(t(posterior_linpred(meta.brms, newdata = brms.newdata, re_formula = NA)))

#Add columns for Environment and Sex
post <- as.data.frame(t(post))
post$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")

#Clean up dataframe
post <- melt(post, id = c("Sex", "Environment"))
post$variable <- NULL

make_text_summary(brms.complete2) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()
Model Parameter Estimate Est.Error Q2.5 Q97.5
b_Intercept 0.222 0.165 -0.104 0.546
b_SexB -0.063 0.064 -0.186 0.061
b_SexF 0.054 0.028 0 0.11
b_EnvironmentStressed -0.12 0.04 -0.198 -0.039 *
b_SexB:EnvironmentStressed 0.164 0.082 0.003 0.322 *
b_SexF:EnvironmentStressed 0.247 0.049 0.151 0.342 *
sd_Outcome__Intercept 0.393 0.117 0.225 0.675 *
sd_Study.ID__Intercept 0.463 0.054 0.37 0.579 *
sd_Taxon__Intercept 0.145 0.139 0.006 0.517 *


Like with the REML approach, from these model results we can test several hypotheses between the categories of environment and sex.

Table S11 Hypothesis tests for the Bayesian model are similar to the REML model (Table S9), with slight differences to CIs.

#Obtain hypothesis estimates
brms.hypothesis <- hypothesis(brms.complete2, c("0 = SexF",
                             "0 = SexF + SexF:EnvironmentStressed",
                             "0 = SexF:EnvironmentStressed + EnvironmentStressed",
                             "0 = EnvironmentStressed",
                             "0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table <- 
  data.frame(brms.hypothesis[["hypothesis"]][["Hypothesis"]],
             brms.hypothesis[["hypothesis"]][["Estimate"]],
             brms.hypothesis[["hypothesis"]][["Est.Error"]],
             brms.hypothesis[["hypothesis"]][["CI.Lower"]],
             brms.hypothesis[["hypothesis"]][["CI.Upper"]],
             brms.hypothesis[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
brms.hypothesis.table <- format(brms.hypothesis.table, digits = 2)
rownames(brms.hypothesis.table) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table %>% pander(split.table = Inf)
  Hypothesis Estimate Est.Error CI.Lower CI.Upper
M vs F, Benign (0)-(SexF) = 0 -0.054 0.028 -0.110 -0.00033 *
M vs F, Stressful (0)-(SexF+SexF:EnvironmentStressed) = 0 -0.301 0.043 -0.384 -0.21661 *
Benign vs Stressful, Female (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 -0.127 0.037 -0.199 -0.05499 *
Benign vs Stressful, Male (0)-(EnvironmentStressed) = 0 0.120 0.040 0.039 0.19776 *
Benign vs Stressful, Both (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 -0.044 0.074 -0.188 0.10162


Using predictions from both REML and Bayesian models we can obtain a figure that plots the mean/median predictions as well as distribution density (Bayesian) and 95 % CI (REML).

#Generate predictions without taxon utilising the previously described function

get.predictions.complete2 <- function(newdata){
  B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Stressed<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(model.complete2, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}

# Get the predictions for each combination of moderators
predictions.complete2 <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed")))
predictions.complete2 <- cbind(predictions.complete2, do.call("rbind", apply(predictions.complete2, 1, get.predictions.complete2))) %>%
  select(Sex, Environment, pred, se, ci.lb, ci.ub) 
for(i in 3:6) predictions.complete2[,i] <- unlist(predictions.complete2[,i])

countpred <- count_(restricted.data, c("Sex", "Environment"))

predictions.complete2 <- left_join(predictions.complete2, countpred, by = c("Sex", "Environment"))
colnames(predictions.complete2) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")
predictions.complete2 <- predictions.complete2 %>%
      mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
         Sex = replace(Sex, Sex == "M", "Male"),
         Sex = replace(Sex, Sex == "F", "Female"),
         Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
         Environment = replace(Environment, Environment == "Unstressed", "Benign"),
         Sex = factor(Sex, levels = c("Male", "Both", "Female")))

#Plot the posterior values from the Bayesian model as density ridges
pd <- position_dodgev(height = 0.3)
posterior.plot <- post %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
  stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
  geom_vline(xintercept = 0, linetype = 2, colour = "black") + 
    ylab("Environment")+
  xlab("\nEffect Size (Hedges' g)")+
  scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  scale_x_continuous(limits = c(-0.75, 1.5), breaks = c(-1, -.5, 0, 0.5, 1, 1.5))+
  
  theme_bw()+
  
  theme(panel.spacing = unit(0.1, "lines"),
        text = element_text(size=16),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(), 
        legend.text = element_text(size=16), 
        legend.title=element_text(size=16, 
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 14),
        axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
        axis.text.y = element_text(angle = 0),
        plot.title = element_text(size = 16))

#Add the REML predictions as circles with error bars
both.plots <- posterior.plot + 
  geom_errorbarh(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))), 
                 aes(x= Prediction, xmin = predictions.complete2$CI.lb,
                     xmax = predictions.complete2$CI.ub, y = Environment,
                     color = Sex), 
                 height = 0, show.legend = F, position = pd)+
  
  geom_point(data = predictions.complete2 %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))), 
             aes(x = Prediction, y = Environment, size=n, fill = Sex), 
             shape=21, color = "grey20", position = pd) +
  
    guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
    scale_size(guide = 'none')+
  
  scale_y_discrete(expand=c(0.075,0))
  
# svg("figures/both_plot.svg", width=6, height=6)
# both.plots
# dev.off()
# #
# pdf("figures/both_plot.pdf",  width = 6, height = 6)
# both.plots
# dev.off()

both.plots


Figure S3: Sexual selection generally increases population fitness, especially for females under stressful conditions. The benefits of sexual selection on fitness for females under stressful conditions are small-medium according to Cohen’s interperetation of effect sizes. Circle size is proportional to sample size (shown below). The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. This figure can also be found in the main manuscript.

Table S12 The REML predictions in the plot above use the following dataframe

predictions.complete2 <- format(predictions.complete2, digits = 2)
predictions.complete2$Prediction = as.numeric(predictions.complete2$Prediction)
predictions.complete2$CI.lb = as.numeric(predictions.complete2$CI.lb)
predictions.complete2$CI.ub = as.numeric(predictions.complete2$CI.ub)
predictions.complete2$n = as.numeric(predictions.complete2$n)

predictions.complete2 %>% pander()
Sex Environment Prediction SE CI.lb CI.ub n
Male Benign 0.213 0.12 -0.017 0.44 119
Both Benign 0.149 0.13 -0.101 0.4 15
Female Benign 0.267 0.12 0.035 0.5 123
Male Stressful 0.092 0.12 -0.145 0.33 23
Both Stressful 0.197 0.13 -0.063 0.46 18
Female Stressful 0.394 0.12 0.159 0.63 38

Estimating Hedges’ g Heterogeneity Using I2

Using an adapted function from https://github.com/daniel1noble/metaAidR we can obtain confidence intervals for total I2 and the individual components of the random effects. There are different methods to obtain estimates of I2. Here we obtain an overall value of I2 that is weighted based on variance and where estimates of heterogeneity are sourced from sigma2 of the respective models. The values are based on the REML models.

I2(model.complete2, restricted.data$var.g) %>% pander(digits = 3)
  I2_Est. 2.5% CI 97.5% CI
Study.ID 61.5 42.9 78.6
Outcome 33.5 15.8 52.7
Taxon 0 0 0
total 94.9 93 96.4

These values indicate that 61.5 % of total heterogeneity is due to the between study heterogeneity and 33.5 % for between outcome heterogeneity between different outcomes. The total I2 is 95 %, a reasonably high I2 value. However this is relatively common in Ecology and Evolution total.


Meta-Analysis on Variance

Obtaining lnCVR and Meta-Analysis Models

This meta-analysis on variation utilises previously described and utilised methods devoleped (Nakagawa et al. 2015; Senior et al. 2016). Our goal is to determine whether the phenotypic variance in fitness related traits is impacted by sexual selection. We would assume that if selection is occuring not only would the trait mean shift in a certain direction but the variance associated with those changes to the mean would also decrease. In this case we use an effect size statistic known as the natural log of the coefficient of variation ratio (lnCVR).

# Firstly, we setup our calculation by creating a a restricted dataset with only unabmiguous fitness outcomes and running the functions developed by Nakagawa et al. 2015: 
Calc.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN){
    
    ES<-log(ESD) - log(EMean) + 1 / (2*(EN - 1)) - (log(CSD) - log(CMean) + 1 / (2*(CN - 1)))
    
    return(ES)
    
}

#for variance of lnCVR

Calc.var.lnCVR<-function(CMean, CSD, CN, EMean, ESD, EN, Equal.E.C.Corr=T){
    
    if(Equal.E.C.Corr==T){
    
        mvcorr<-cor.test(log(c(CMean, EMean)), log(c(CSD, ESD)))$estimate
    
        S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * mvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * mvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))
    
    }
    else{
        
        Cmvcorr<-cor.test(log(CMean), log(CSD))$estimate
        Emvcorr<-cor.test(log(EMean), (ESD))$estimate
    
        S2<- CSD^2 / (CN * (CMean^2)) + 1 / (2 * (CN - 1)) - 2 * Cmvcorr * sqrt((CSD^2 / (CN * (CMean^2))) * (1 / (2 * (CN - 1)))) + ESD^2 / (EN * (EMean^2)) + 1 / (2 * (EN - 1)) - 2 * Emvcorr * sqrt((ESD^2 / (EN * (EMean^2))) * (1 / (2 * (EN - 1))))      
        
        
    }
    return(S2)
    
}


# Secondly, we utilise those formulas to obtain lnCVR and var.CVR for all applicable effect sizes. Noting that not all of the dataset has means, SD and n; some were calculated from summary statistics and are not able to have lnCVR calculated:

#Calculate lnCVr and var.lnCVr
#for lnCVR
restricted.data$lnCVr <- Calc.lnCVR(restricted.data$mean.low, restricted.data$sd.low, restricted.data$n.low, restricted.data$mean.high, restricted.data$sd.high, restricted.data$n.high)

#for variance in lnCVR
restricted.data$var.lnCVr <- Calc.var.lnCVR(restricted.data$mean.low, restricted.data$sd.low, restricted.data$n.low, restricted.data$mean.high, restricted.data$sd.high, restricted.data$n.high, Equal.E.C.Corr=T) #Equal.E.C.Corr = T assumes that the correlaiton between mean and sd (Taylor's Law) is equal for the mean and control groups


restricted.data2 <- restricted.data 

Although not previously done extensively, it seems that the best way to conduct this analysis is not through subsetting but through utilising model predictions as we did with Hedges’ g previously, that way we retain the same methodology in model structure and test the same hypotheses. This can be done be utilising the same predict function but for lnCVR and var.lnCVR. For the brms model we can obtain predictions manually or through the fitted.brmsfit.

Multilevel-model using lnCVR and REML:

variance.model <- rma.mv(lnCVr, V = var.lnCVr, mods = ~ 1 + Sex*Environment, 
                          random = list(~ 1 | Taxon,
                                        ~ 1 | Study.ID,
                                        ~ 1 | Outcome), 
                         method = "REML", data = restricted.data2)
summary(variance.model, digits = 2)
## 
## Multivariate Meta-Analysis Model (k = 277; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc  
## -6796.24  13592.48  13610.48  13642.90  13611.17  
## 
## Variance Components: 
## 
##            estim  sqrt  nlvls  fixed    factor
## sigma^2.1   0.06  0.25      6     no     Taxon
## sigma^2.2   0.15  0.38     46     no  Study.ID
## sigma^2.3   0.15  0.38     11     no   Outcome
## 
## Test for Residual Heterogeneity: 
## QE(df = 271) = 19525.52, p-val < .01
## 
## Test of Moderators (coefficient(s) 2:6): 
## QM(df = 5) = 3845.00, p-val < .01
## 
## Model Results:
## 
##                           estimate    se    zval  pval  ci.lb  ci.ub     
## intrcpt                       0.07  0.18    0.41  0.68  -0.28   0.42     
## SexB                         -0.05  0.04   -1.33  0.18  -0.12   0.02     
## SexF                         -0.15  0.01  -11.40  <.01  -0.18  -0.13  ***
## EnvironmentStressed          -0.21  0.02  -10.53  <.01  -0.25  -0.17  ***
## SexB:EnvironmentStressed     -0.25  0.04   -5.94  <.01  -0.34  -0.17  ***
## SexF:EnvironmentStressed     -0.75  0.02  -31.12  <.01  -0.80  -0.70  ***
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Again, we use brms to obtain Bayesian model estimates. For this model the R2 is 0.34 (95% CIs = 0.32-0.36).

#When knitting this markdown this model stalls
#Use brm to create a model similar to the one used in the REML approach. 
variance.brms <- brm(lnCVr| se(SE.v)  ~ 1 + Sex * Environment #response is Hedges' g and the standard error associated with it (SE), sex, environment and their interaction are moderators
                + (1|Taxon) #group level effects
                + (1|Study.ID)
                + (1|Outcome),
                family = "gaussian", #default
                seed = 1,
                cores = 4, chains = 4, iter = 4000, #Run 4 chains in parrallel for 4000 iterations (2000 are burn in)
                control = list(adapt_delta = 0.999, max_treedepth = 15),
                data = restricted.data2 %>% mutate(SE.v = sqrt(var.lnCVr)))

Table S13 Model estimates, including random effect sigma value for the model of phenotypic variance (lnCVR)

var.brms <- readRDS(file = "data/variance.brms.rds") #Avoid re-running model above
post.variance <- (posterior_samples(var.brms, 
                           pars = c("b_Intercept", "b_SexB", "b_SexF", 
                                    "b_EnvironmentStressed", "b_SexB:EnvironmentStressed", 
                                    "b_SexF:EnvironmentStressed")) %>%
         mutate(both_benign = b_Intercept + b_SexB,
         both_stressful = b_Intercept + b_SexB + b_EnvironmentStressed + `b_SexB:EnvironmentStressed`,
         male_benign = b_Intercept,
         male_stressful = b_Intercept + b_EnvironmentStressed,
         female_benign = b_Intercept + b_SexF,
         female_stressful = b_Intercept + b_SexF + b_EnvironmentStressed + `b_SexF:EnvironmentStressed`))[,-(1:6)]

#Add columns for Environment and Sex
post.variance <- as.data.frame(t(post.variance))
post.variance$Sex <- c("Both", "Both", "Male", "Male", "Female", "Female")
post.variance$Environment <- c("Benign", "Stressful", "Benign", "Stressful", "Benign", "Stressful")

#Clean up dataframe
post.variance <- melt(post.variance, id = c("Sex", "Environment"))
post.variance$variable <- NULL


make_text_summary(var.brms) %>% add_significance_stars() %>% tibble::rownames_to_column("Model Parameter") %>% pander()
Model Parameter Estimate Est.Error Q2.5 Q97.5
b_Intercept 0.067 0.278 -0.501 0.599
b_SexB -0.048 0.036 -0.118 0.023
b_SexF -0.153 0.014 -0.18 -0.127 *
b_EnvironmentStressed -0.212 0.02 -0.25 -0.173 *
b_SexB:EnvironmentStressed -0.254 0.042 -0.338 -0.176 *
b_SexF:EnvironmentStressed -0.749 0.024 -0.798 -0.702 *
sd_Outcome__Intercept 0.448 0.131 0.27 0.77 *
sd_Study.ID__Intercept 0.393 0.049 0.31 0.503 *
sd_Taxon__Intercept 0.418 0.324 0.047 1.244 *


Predictions based on the REML and Bayesian model can then be generated in the same way as for Hedges’g. Here, negative values of lnCVR indicate a narrowing (decrease) in phenotypic variance as a result of sexual selection.

#Generate predictions
get.predictions.variance <- function(newdata){
  B<-0; F<-0; Stressed<-0; interaction1<-0; interaction2<-0; interaction3<-0
  if(newdata[1] == "B") B<-1 
  if(newdata[1] == "F") F<-1 
  if(newdata[2] == "Stressed") Stressed<-1
  if(newdata[1] == "B" & newdata[2] == "Stressed") interaction1<-1
  if(newdata[1] == "F" & newdata[2] == "Stressed") interaction2<-1

  predict(variance.model, newmods=c(B, F, Stressed, interaction1=interaction1, interaction2=interaction2))
}
# Get the predictions for each combination of moderators
predictions.variance <- as.data.frame(expand.grid(Sex = c("M", "B", "F"),
                           Environment = c("Unstressed", "Stressed")))
predictions.variance <- cbind(predictions.variance, do.call("rbind", apply(predictions.variance, 1, get.predictions.variance))) %>%
  select(Sex, Environment, pred, se, ci.lb, ci.ub) 
for(i in 3:6) predictions.variance[,i] <- unlist(predictions.variance[,i])

countpred = count_(restricted.data2, c("Sex", "Environment"))

predictions.variance <- left_join(predictions.variance, countpred, by = c("Sex", "Environment"))

#Change names to make them more clear
predictions.variance <- predictions.variance %>% 
      mutate(Sex = replace(as.character(Sex), Sex == "B", "Both"),
         Sex = replace(Sex, Sex == "M", "Male"),
         Sex = replace(Sex, Sex == "F", "Female")) %>%
  mutate(Environment = replace(as.character(Environment), Environment == "Stressed", "Stressful"),
         Environment = replace(Environment, Environment == "Unstressed", "Benign"))

colnames(predictions.variance) <- c("Sex", "Environment", "Prediction", "SE", "CI.lb", "CI.ub", "n")

#And plot the results, first for the posterior results of the brms model then for the metafor predictions

var.plot.posterior <- post.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))) %>% ggplot()+
  stat_density_ridges(aes(x=value, y = Environment, fill = Sex), alpha = 0.65, scale = 0.6, position = position_nudge(y = 0.15), height = 10, show.legend = F, quantile_lines = T, quantiles = 2)+
  geom_vline(xintercept = 0, linetype = 2, colour = "black") + 
  ylab("Environment\n")+
  xlab("\nPhenotypic Variance (lnCVR)")+
  scale_x_continuous(limits = c(-2.1, 1.2), breaks = c(-2, -1.5, -1, -0.5, 0, 0.5, 1))+
  scale_fill_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  scale_color_manual(values = c("Male" = "#e41a1c", "Female" = "#377eb8", "Both" = "#4daf4a"))+
  
  theme_bw()+
  
  theme(panel.spacing = unit(0.1, "lines"),
        text = element_text(size=16),
        panel.border= element_blank(),
        axis.line=element_line(), 
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(), 
        legend.text = element_text(size=16), 
        legend.title=element_text(size=16, 
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 14),
        axis.title.y = element_text(size = 16, hjust = 0.35, margin = margin(r=-10)),
        axis.text.y = element_text(angle = 0),
        plot.title = element_text(size = 16))


both.var.plots <- var.plot.posterior +
  geom_errorbarh(data = predictions.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))), 
                 aes(x = Prediction, xmin = predictions.variance$CI.lb, 
                     xmax = predictions.variance$CI.ub, y = Environment,
                     color = Sex), 
                 height = 0, position = pd, show.legend = F) +
  geom_point(data = predictions.variance %>% mutate(Sex = factor(Sex, levels = c("Male", "Both", "Female"))), 
             aes(x = Prediction, y = Environment, size=n, fill = Sex), 
             shape=21, color = "grey20", position = pd) +
  guides(fill = guide_legend(reverse=T, override.aes = list(size = 7.5)))+
  scale_size(guide = 'none')+  


  scale_y_discrete(expand=c(0.075,0))

# svg("figures/both.var.plots.svg", width=6, height=6)
# both.var.plots
# dev.off()
# 
# pdf("figures/both.var.plots.pdf",  width = 6, height = 6)
# both.var.plots
# dev.off()

both.var.plots


Figure S4: Phenotypic variation changes under sexual selection in stressful environments. For females under stressful conditions phenotypic variation decreases (narrows). While for males in stressful environments it increases. For outcomes that measured a mix of both males and females (pooled samples) in stressful environments phenotypic variation decreased slightly. The REML predictions are shown as circles with error bars and the Bayesian predictions as density ridges. Circle size is proportional to sample size.

Table S14 The REML predictions in the plot above use the following dataframe.

predictions.variance <- format(predictions.variance, digits = 2)
predictions.variance %>% pander()
Sex Environment Prediction SE CI.lb CI.ub n
Male Benign 0.073 0.18 -0.28 0.424 119
Both Benign 0.026 0.18 -0.33 0.381 15
Female Benign -0.080 0.18 -0.43 0.271 123
Male Stressful -0.138 0.18 -0.49 0.214 23
Both Stressful -0.440 0.18 -0.80 -0.081 18
Female Stressful -1.040 0.18 -1.39 -0.689 38

Hypothesis Tests For Phenotypic Variation Model

Like we did for Hedges g’ we can also conduct hypothesis tests between categorical groups to inform us if there is a difference between two groups of interest (e.g. females in benign and females in stressful environments). Here we present Bayesian and REML hypothesis predictions. Positive values indivate the first term in the hypothesis is larger (which would be male for rows 1-2 or benign for rows 3-5).

Table S15 Bayesian hypothesis tests between categorical groups for phenotypic variation (lnCVR)

#Obtain hypothesis estimates
brms.hypothesis.var <- hypothesis(var.brms, c("0 = SexF",
                             "0 = SexF + SexF:EnvironmentStressed",
                             "0 = SexF:EnvironmentStressed + EnvironmentStressed",
                             "0 = EnvironmentStressed",
                             "0 = SexB:EnvironmentStressed + EnvironmentStressed"))
#Format into dataframe
brms.hypothesis.table.var <- 
  data.frame(brms.hypothesis.var[["hypothesis"]][["Hypothesis"]],
             brms.hypothesis.var[["hypothesis"]][["Estimate"]],
             brms.hypothesis.var[["hypothesis"]][["Est.Error"]],
             brms.hypothesis.var[["hypothesis"]][["CI.Lower"]],
             brms.hypothesis.var[["hypothesis"]][["CI.Upper"]],
             brms.hypothesis.var[["hypothesis"]][["Star"]])
colnames(brms.hypothesis.table.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", " ")
row.names(brms.hypothesis.table.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
brms.hypothesis.table.var <- format(brms.hypothesis.table.var, digits = 2)
brms.hypothesis.table.var %>% pander(split.table = Inf)
  Hypothesis Estimate Est.Error CI.Lower CI.Upper
M vs F, Benign (0)-(SexF) = 0 0.15 0.014 0.13 0.18 *
M vs F, Stressful (0)-(SexF+SexF:EnvironmentStressed) = 0 0.90 0.022 0.86 0.95 *
Benign vs Stressful, Female (0)-(SexF:EnvironmentStressed+EnvironmentStressed) = 0 0.96 0.017 0.93 0.99 *
Benign vs Stressful, Male (0)-(EnvironmentStressed) = 0 0.21 0.020 0.17 0.25 *
Benign vs Stressful, Both (0)-(SexB:EnvironmentStressed+EnvironmentStressed) = 0 0.47 0.038 0.39 0.54 *

Table S16 REML hypothesis tests between categorical groups for phenotypic variation (lnCVR)

#anova where you specify the values based on the list of moderators
anova.1 = anova(variance.model, L=c(0, 0, -1, 0, 0, 0)) 
anova.2 = anova(variance.model, L=c(0, 0, -1, 0, 0, -1))
anova.3 = anova(variance.model, L=c(0, 0, 0, -1, 0, -1))
anova.4 = anova(variance.model, L=c(0, 0, 0, -1, 0, 0))
anova.5 = anova(variance.model, L=c(0, 0, 0, -1, -1, 0))

anova.list.var <- list(anova.1, anova.2, anova.3, anova.4, anova.5)

anova.frame.var <- t(data.frame(lapply(anova.list.var, function(x) {
  data.frame(x[["hyp"]],
  x[["Lb"]],
  x[["se"]],
  x[["Lb"]] - 1.96*x[["se"]],
  x[["Lb"]] + 1.96*x[["se"]],
  x[["pval"]])
})))
anova.frame.var <- as.data.frame(split(anova.frame.var, rep(1:6)))
colnames(anova.frame.var) <- c("Hypothesis", "Estimate", "Est.Error", "CI.Lower", "CI.Upper", "pval")
anova.frame.var$Estimate <- as.numeric(levels(anova.frame.var$Estimate))[anova.frame.var$Estimate]
anova.frame.var$Est.Error <- as.numeric(levels(anova.frame.var$Est.Error))[anova.frame.var$Est.Error]
anova.frame.var$CI.Lower <- as.numeric(levels(anova.frame.var$CI.Lower))[anova.frame.var$CI.Lower]
anova.frame.var$CI.Upper <- as.numeric(levels(anova.frame.var$CI.Upper))[anova.frame.var$CI.Upper]
anova.frame.var$pval <- as.numeric(levels(anova.frame.var$pval))[anova.frame.var$pval]
anova.frame.var <- format(anova.frame.var, digits = 2)
anova.frame.var$star <- c("*", "*", "*", "*", "*")
colnames(anova.frame.var)[colnames(anova.frame.var)=="star"] <- " "
anova.frame.var$pval <- NULL
row.names(anova.frame.var) <- c("M vs F, Benign", "M vs F, Stressful", "Benign vs Stressful, Female", "Benign vs Stressful, Male", "Benign vs Stressful, Both")
anova.frame.var %>% pander(split.table = Inf)
  Hypothesis Estimate Est.Error CI.Lower CI.Upper
M vs F, Benign -SexF = 0 0.15 0.013 0.13 0.18 *
M vs F, Stressful -SexF - SexF:EnvironmentStressed = 0 0.90 0.022 0.86 0.95 *
Benign vs Stressful, Female -EnvironmentStressed - SexF:EnvironmentStressed = 0 0.96 0.017 0.93 0.99 *
Benign vs Stressful, Male -EnvironmentStressed = 0 0.21 0.020 0.17 0.25 *
Benign vs Stressful, Both -EnvironmentStressed - SexB:EnvironmentStressed = 0 0.47 0.039 0.39 0.54 *

Estimating lnCVR Heterogeneity Using I2

Similar to the meta-analysis on Hedges’ g we can obtain I2 for lnCVR REML model. In this case (compared to Hedges’ g) we see Taxon has more of a variable effect on overall I2 estimates, although the overall I2 remains relatively similar (95.4 %).

I2(variance.model, restricted.data2$var.g) %>% pander(digits = 3)
  I2_Est. 2.5% CI 97.5% CI
Taxon 16.6 3.1 37.2
Study.ID 40.1 23.5 60.2
Outcome 38.7 17.1 60.7
total 95.4 93 97.1

Publication Bias

Funnel plots and Egger’s Test

Here we check for publication bias with a funnel plot. Note that the trim and fill or Eggers test method does not work with rma.mv objects. We can perform Eggers test using the regtest() function. This tests for asymmetry via assessing relationships between effect size and a specified predictor. Because the Eggers test does not work for rma.mv objects we remove the random effects and run with Sex * Environment as moderators.

standard.model <- rma(g, var.g, 
                      mods = ~ Sex * Environment, 
                      data=prelim.data)
regtest(standard.model)
## 
## Regression Test for Funnel Plot Asymmetry
## 
## model:     mixed-effects meta-regression model
## predictor: standard error
## 
## test for funnel plot asymmetry: z = 6.2210, p < .0001

We can use ggplot for creating a funnel plot. The outline taken from is taken from: https://sakaluk.wordpress.com/2016/02/16/7-make-it-pretty-plots-for-meta-analysis/

#Using residuals for the funnel plot means that we need to generate residuals (intercept only)

forest.model <- rma.mv(g, var.g,
                       mods = ~ 1,
                       random = list(~ 1 | Study.ID,
                                       ~ 1 | Outcome),
                       method = "REML",
                       data = prelim.data)

# Obtain residuals
resstandards <- (rstandard.rma.mv(forest.model,
                                   type="response"))

# Obtain grand mean effect size 
grand.mean <- as.numeric(forest.model$b) 

# Create new df with residuals replacing raw
df.forest.model <- prelim.data
df.forest.model$g <- resstandards$resid + grand.mean 
df.forest.model$sei <- resstandards$se

# Funnel plot for all outcome classes

make.funnel <- function(dataset, model){
  
  apatheme <- theme_bw() +  #My APA-format theme
    theme(panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          axis.line = element_line(),
          text = element_text(family = 'Times'),
          legend.position = 'none')
  
  estimate <- model$b
  SE <- model$se
  se.seq <- seq(0, max(sqrt(dataset$var.g)), 0.001)
  dfCI <- data.frame(ll95 = estimate - (1.96 * se.seq), 
                     ul95 = estimate + (1.96 * se.seq), 
                     ll99 = estimate - (3.29 * se.seq), 
                     ul99 = estimate + (3.29 * se.seq), 
                     se.seq = se.seq, 
                     meanll95 = estimate - (1.96 * SE), 
                     meanul95 = estimate + (1.96 * SE))
  
  ggplot(dataset, aes(x = sqrt(var.g), y = g)) +
    geom_point(size=1.5, shape = 21, color= "grey20") +
    xlab("Standard Error") + ylab("Effect Size (Hedges' g)") +
    geom_line(aes(x = se.seq, y = ll95), linetype = 'dotted', data = dfCI) + # confidence lines
    geom_line(aes(x = se.seq, y = ul95), linetype = 'dotted', data = dfCI) +
    geom_line(aes(x = se.seq, y = ll99), linetype = 'dashed', data = dfCI) +
    geom_line(aes(x = se.seq, y = ul99), linetype = 'dashed', data = dfCI) +
    #Now plot dotted lines corresponding to the 95% CI of your meta-analytic estimate
    geom_segment(aes(x = min(se.seq), y = meanll95, xend = max(se.seq), yend = meanll95), linetype='dotdash', data=dfCI, colour = "tomato",size =0.75) +
    geom_segment(aes(x = min(se.seq), y = meanul95, xend = max(se.seq), yend = meanul95), linetype='dotdash', data=dfCI, colour = "tomato",size=0.75) +
    scale_x_reverse() +
    # scale_y_continuous(breaks = seq(-1.25,2,0.25)) + #Choose values that work for you based on your data
    coord_flip() +
    scale_fill_brewer(palette = "Set1")+

    theme_bw()+
  
    theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        text = element_text(size=14),
        axis.line=element_line(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(hjust = 0.5, size = 12),
        axis.title.y = element_text(size = 12))

}

funnel.plot <- make.funnel(df.forest.model, forest.model)

ggsave(plot = funnel.plot, filename = "figures/funnel_plot.eps", height = 7.5, width = 10)
funnel.plot

# svg("figures/funnel_plot.svg", width=8, height=6)
# funnel.plot
# dev.off()


Figure S5: A funnel plot of 459 effect sizes shows asymmetry, indicating potential publication bias, egger’s regression test for funnel plot asymmetry also suggests the plot is asymmetrical (z = 6.2210, p < .0001). The asymmetry appears to be sourced from a spread of positive effect sizes outside the funnel and of varying degrees of precision. Counter to expectations of publication bias these positive studies are not just ‘low precision, large effect’ results. Funnel plot asymmetry may also be due to heterogeneity, which in this study is high due to the many species and outcomes measured.

Journal Impact Factor

If we see a positive trend with effect size and Journal Impact Factor (JIF) it may represent publication bias whereby significant (positive) results are published more readily and in more circulated journals and non-confirmitory or negative results are not published or publiushed in lower impact journals. Our journal impact factor dataset is not evenly distributed as several publications in Nature (JIF ~ 40) are much larger than the next highest JIF (~11).

JIF.plot <- ggplot(data = prelim.data, aes(x=JIF, y=g))+
  geom_jitter(color='darkgreen', alpha=0.4, aes(size = (1/(var.g))/sum((1/(var.g)))*100), show.legend = FALSE)+
  geom_hline(yintercept=0, linetype = 'dotted')+
  geom_smooth(method='lm', color='black', linetype="solid")+
  scale_x_log10(limits = c(-5,40), breaks = c(0, 1, 2, 5, 10, 20, 40))+
  labs(size = 'Weight (%)', y='Effect size (Hedges g)', x= 'Journal Impact Factor (logarithmic scale)')+ 
  
  theme_bw()+
  
    theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        text = element_text(size=14),
        axis.line=element_line(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

# dev.off()
# 
# svg("figures/JIF_Plot.svg", width=4, height=4)
# JIF.plot
# dev.off()

JIF.plot


Figure S6: Journal impact factor does not show a noticable correlation with effect size. The positive slope shown here is due to several effect sizes published in a high impact journal. Most papers were published in discipline specific journals such as Evolution and Journal of Evolutionary Biology. Circle size is proportional to weight (%) of study.

We can test the effect of JIF on ES with a simple linear model:

JIFlm <- lm(g ~ log(JIF), data = prelim.data)
summary(JIFlm)
## 
## Call:
## lm(formula = g ~ log(JIF), data = prelim.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6439 -0.3368 -0.1023  0.2561  2.9384 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept)  0.01680    0.13823   0.122    0.903
## log(JIF)     0.10850    0.09393   1.155    0.249
## 
## Residual standard error: 0.6755 on 437 degrees of freedom
##   (20 observations deleted due to missingness)
## Multiple R-squared:  0.003044,   Adjusted R-squared:  0.0007625 
## F-statistic: 1.334 on 1 and 437 DF,  p-value: 0.2487

This shows that JIF does not have a significant effect on effect sizes from the published study

Time-lag Bias

We can also look at the time-lag bias, which suggests effect size decreases over time. Again, because one publication from 1980 is well before the next publication in the late 1990s we see a very uneven distribution of data points.

time.plot <- prelim.data %>% 
  ggplot(aes(x=Year, y=g))+
  geom_jitter(color='darkorange', alpha=.5, aes(size = (1/(var.g))/sum((1/(var.g)))*100), show.legend = FALSE)+
  geom_hline(yintercept=0, linetype = 'dotted')+
  geom_smooth(method='lm', color='black')+
  labs(size = 'Weight (%)', y='Effect size (Hedges g)', x= 'Year')+
    theme_bw()+
    theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        axis.line=element_line(),
        text = element_text(size=14),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

time.plot

# svg("figures/Time_Plot.svg", width=4, height=4)
# time.plot
# dev.off()


Figure S7: The effect size dataset shows little to no signs of the time-lag bias as the average effect sizes from published studies remains consistent across the previous two decades. Circle size is proportional to weight of study (%).

We can also test the effect of publication year on ES with a simple linear model (similar to the one for JIF:

Yearlm <- lm(g ~ Year, data = prelim.data)
summary(Yearlm)
## 
## Call:
## lm(formula = g ~ Year, data = prelim.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6771 -0.3504 -0.1232  0.2934  2.9209 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)
## (Intercept) 13.268558  12.784999   1.038    0.300
## Year        -0.006507   0.006361  -1.023    0.307
## 
## Residual standard error: 0.6942 on 457 degrees of freedom
## Multiple R-squared:  0.002285,   Adjusted R-squared:  0.0001013 
## F-statistic: 1.046 on 1 and 457 DF,  p-value: 0.3069

This shows that publication year does not have a significant effect on effect sizes from the published study.


Other Moderators

Blinding

In addition to publication bias, other forms of bias may exist within studies. We initially collected data on whether studies were blind or not. Although not many studies (n=8) used blinding there was multiple effect sizes reported in these studies, thus we can visualise whether blinding affects the effect sizes from the model. Blinding was regarded as a redundant predictor in the model (estimate = 0.0287, p = 0.8974) and was dropped.

blind.plot <- df.forest.model %>% ggplot(aes(x=Blinding, y=g))+
  geom_boxplot(outlier.shape = NA)+
  geom_jitter(aes(fill=Blinding, size = (1/(var.g))/sum((1/(var.g)))*100), shape=21, color='grey20')+
  geom_hline(yintercept=0, linetype = 'dotted') + 
  scale_fill_brewer(palette = "Set2")+
  labs(y="Effect size (Hedges' g)", x= 'Blinding', size = 'Weight (%)')+
  guides(fill=FALSE, size = guide_legend(override.aes = list(fill = "#66c2a5")))+
  theme_bw()+
  theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        axis.line=element_line(),
        text = element_text(size=14),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

#ggsave(plot = blind.plot, filename = "figures/blind_plot.eps", height = 6, width = 8)
blind.plot


Figure S9: Blinding does not appear to alter the magnitude or direction of effect sizes for the studies used in this meta-analysis. However, this should not be viewed as evidence against the validity of blinding as a research method.


Generations

We recorded the number of generations of experimental exolution each study used. The number of generations proved a negligible predictor in the meta-analytic models (estimate = 0.0019, p = 0.2341). The effect sizes are plotted against the generation at which the effect size was extracted.

generations.plot <- restricted.data %>% ggplot(aes(x=Generations, y=g))+
  geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
  ylim(-3.5,3.5)+
  geom_hline(yintercept=0, linetype="dashed") + 
  scale_fill_brewer(palette = "Set3")+
  geom_smooth(method = 'lm', color='black')+
  labs(y="Effect size (Hedges' g)", x= 'Generations', size= 'Weight (%)')+
  theme_bw()+
  theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        axis.line=element_line(),
        text = element_text(size=14),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

#ggsave(plot = generations.plot, filename = "figures/generations_plot.eps", height = 7.5, width = 10)

# svg("figures/Generations_Plot.svg", width=10, height=7.5)
# generations.plot
# dev.off()

generations.plot


Figure S10: The number of generations an experimental evolution procedure is run for does not appear to affect the magnitude or direction of the effect size from the fitness related outcome measured at that point.
A linear model shows next to no effect of generations on effect size:

summary(lm(g ~ Generations, data = restricted.data))
## 
## Call:
## lm(formula = g ~ Generations, data = restricted.data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.7172 -0.3694 -0.1293  0.3132  2.6580 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.2496047  0.0638614   3.909 0.000112 ***
## Generations -0.0003535  0.0015300  -0.231 0.817439    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.6679 on 334 degrees of freedom
## Multiple R-squared:  0.0001598,  Adjusted R-squared:  -0.002834 
## F-statistic: 0.05337 on 1 and 334 DF,  p-value: 0.8174

Kawecki et al. (2012) reviewed the field of experimental evolution and noted that changes to variation may need longer generations to become apparent. The following graph looks at the relationship between number of generations and lnCVr:

generations.plot.var <- restricted.data2 %>% ggplot(aes(x=Generations, y=lnCVr))+
  geom_jitter(shape=21, color = "grey20", size=2, aes(fill=Taxon))+
  ylim(-3.5,3.5)+
  geom_hline(yintercept=0, linetype="dashed") + 
  scale_fill_brewer(palette = "Set3")+
  geom_smooth(method = 'lm', color='black')+
  labs(y='Effect size (lnCVR)', x= 'Generations', size= 'Weight (%)')+
  theme_bw()+
  theme(panel.spacing = unit(0.5, "lines"),
        panel.border= element_blank(),
        text = element_text(size=14),
        axis.line=element_line(),
        panel.grid.major.x = element_blank(),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.text = element_text(size=12),
        legend.title=element_text(size=12,
                                  face = "bold"),
        axis.title.x = element_text(size = 12),
        axis.title.y = element_text(size = 12))

# ggsave(plot = generations.plot, filename = "figures/generations_plot.eps", height = 7.5, width = 10)
generations.plot.var

Figure S11: Phenotypic variation (lnCVR) is not affected by the number of generations an experiment is ran for.

summary(lm(lnCVr ~ Generations, data = restricted.data2))
## 
## Call:
## lm(formula = lnCVr ~ Generations, data = restricted.data2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -2.6285 -0.2647  0.0093  0.2299  3.1872 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)  
## (Intercept) -0.122280   0.063333  -1.931   0.0545 .
## Generations  0.001404   0.001572   0.894   0.3724  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.5653 on 275 degrees of freedom
##   (59 observations deleted due to missingness)
## Multiple R-squared:  0.002895,   Adjusted R-squared:  -0.0007311 
## F-statistic: 0.7984 on 1 and 275 DF,  p-value: 0.3724

R Session Information

This section shows the operating system and R packages attached during the production of this document

sessionInfo() %>% pander

R version 3.3.1 (2016-06-21)

**Platform:** x86_64-apple-darwin13.4.0 (64-bit)

locale: en_AU.UTF-8||en_AU.UTF-8||en_AU.UTF-8||C||en_AU.UTF-8||en_AU.UTF-8

attached base packages: grid, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: bindrcpp(v.0.2), cowplot(v.0.9.1), glmulti(v.1.0.7), rJava(v.0.9-9), MuMIn(v.1.40.0), knitr(v.1.18), metaAidR(v.0.0.0.9000), brmstools(v.0.5.1), bayesplot(v.1.5.0), backports(v.1.1.2), brms(v.2.3.1), Rcpp(v.0.12.14), rstan(v.2.17.3), StanHeaders(v.2.17.2), ggridges(v.0.5.0), RColorBrewer(v.1.1-2), reshape2(v.1.4.3), ggrepel(v.0.7.0), kableExtra(v.0.7.0), ggthemes(v.3.4.0), ggplot2(v.2.2.1), forestplot(v.1.7.2), checkmate(v.1.8.5), magrittr(v.1.5), car(v.2.1-5), lme4(v.1.1-15), dplyr(v.0.7.4), plyr(v.1.8.4), metafor(v.2.0-0), Matrix(v.1.2-6), compute.es(v.0.2-4), tidyr(v.0.7.2) and pander(v.0.6.1)

loaded via a namespace (and not attached): nlme(v.3.1-128), matrixStats(v.0.53.1), pbkrtest(v.0.4-7), xts(v.0.10-1), threejs(v.0.3.1), httr(v.1.3.1), rprojroot(v.1.3-2), tools(v.3.3.1), R6(v.2.2.2), DT(v.0.4), lazyeval(v.0.2.1), mgcv(v.1.8-12), colorspace(v.1.3-2), nnet(v.7.3-12), gridExtra(v.2.3), Brobdingnag(v.1.2-4), rvest(v.0.3.2), quantreg(v.5.33), SparseM(v.1.77), shinyjs(v.1.0), xml2(v.1.1.1), labeling(v.0.3), colourpicker(v.1.0), scales(v.0.5.0), dygraphs(v.1.1.1.4), mvtnorm(v.1.0-6), readr(v.1.1.1), stringr(v.1.2.0), digest(v.0.6.13), minqa(v.1.2.4), rmarkdown(v.1.8), base64enc(v.0.1-3), pkgconfig(v.2.0.1), htmltools(v.0.3.6), htmlwidgets(v.1.2), rlang(v.0.1.6), shiny(v.1.0.5), bindr(v.0.1), zoo(v.1.8-1), gtools(v.3.5.0), crosstalk(v.1.0.0), inline(v.0.3.14), loo(v.2.0.0), munsell(v.0.4.3), abind(v.1.4-5), stringi(v.1.1.5), yaml(v.2.1.16), MASS(v.7.3-45), parallel(v.3.3.1), miniUI(v.0.1.1), lattice(v.0.20-33), splines(v.3.3.1), hms(v.0.3), igraph(v.1.1.2), markdown(v.0.8), shinystan(v.2.4.0), codetools(v.0.2-14), stats4(v.3.3.1), rstantools(v.1.5.0), glue(v.1.1.1), evaluate(v.0.10.1), nloptr(v.1.0.4), httpuv(v.1.3.5), MatrixModels(v.0.4-1), gtable(v.0.2.0), purrr(v.0.2.4), assertthat(v.0.2.0), mime(v.0.5), xtable(v.1.8-2), coda(v.0.19-1), rsconnect(v.0.8.8), viridisLite(v.0.2.0), tibble(v.1.3.4), shinythemes(v.1.1.1) and bridgesampling(v.0.4-0)


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